Anticipatory planning for end of life requires a common language for discussion among patients, families, and professionals. Studies show that early Palliative Care (PC) interventions based on a problem-oriented approach can improve quality of life, support decision-making, and optimize the timing of medical treatment and transition to hospice services. The aim of this quality-improvement project was to develop a pragmatic structure meeting all clinical settings and populations needs. Based on the Medical Research Council (MRC) framework, a literature review identifying approaches commonly used in PC was performed. In addition, more than 500 hospital-based interprofessional consultations were analyzed. Identified themes were structured and compared to published approaches. We evaluated the clinical usefulness of this structure with an online survey among professionals. The emerged ‘SENS’-structure stands for: Symptoms patients suffer from; End-of-life decisions; Network around the patient delivering care; and Support for the carer. Evaluation among professionals has confirmed that the ‘SENS’-structure covers all relevant areas for anticipatory planning in PC. ‘SENS’ is useful in guiding patient-centered PC conversations and pragmatic anticipatory planning, alongside the regular diagnosis-triggered approach in various settings. Following this approach, ‘SENS’ may facilitate systematic integration of PC in clinical practice. Depending on clearly defined outcomes, this needs to be confirmed by future randomized controlled studies. Anticipatory planning for the end of life, including Advance Care Planning (ACP) as a component of Palliative Care (PC), has become a core element in western healthcare systems to improve the care for people with advanced life-limiting illnesses . Early iHowever, the most effective but still easy to use thematic structure in an ePC intervention to identify and prioritize areas used in an ePC intervention  and how We present how our team developed the so-called ‘SENS’-structure —a thematic structure for anticipatory planning as the essence of ePC interventions. The purpose of our PC hospital-wide, quality-enhancing project was to develop a pragmatic and, by the interprofessional team, concise assessment structure to identify patients’ major concerns and priorities, as well as their main resources. The structure should (a) be easy to use, (b) support mutual agreed multidimensional care plan to facilitate self-efficacy in medically challenging situations, (c) facilitate the evaluation of interventions toward defined goals of care, and (d) ease the information flow between general and specialized PC. Furthermore, it should eventually serve as an educational structure for patients and health professionals, as well as support financial reimbursement for PC interventions.We explore the clinical value and implementation of the ‘SENS’-structure and its usefulness for planning, documentation, evaluation, and education primarily in the acute clinical setting from the perspective of professionals. We are going to discuss its uptake, challenges, and opportunities. Its validity and effects in the interaction with patients will be the subject of future articles.For the development of the ‘SENS’-structure , we usedAs part of the hospital quality improvement project in St.Gallen and Bern , we follPre-clinical phase (2008–2010): Based on an exploration of the literature, we tried to identify clinical approaches that were commonly used to structure care-planning conversations thematically. We used the search terms “palliative care”, “needs assessment”, “assessment of health care needs”, “guideline health planning”, and “advance care planning” using the Boolean operators “and/or”, respectively. Our interprofessional team of PC experts critically evaluated these approaches for their clinical application and practicability. Derived common goals of PC as defined within the Swiss national PC strategy  served aPhase 1: During the modelling phase 2010–2012), we tested the feasibility and acceptability of the first structure in daily clinical practice. For that, we systematically reviewed, retrospectively, more than 500 records of documented inpatient PC consultations, performed by our interprofessional PC team with patients disregarding their disease and their families. We compared and ordered clusters of common themes into the goals of PC [10–2012, Phase 2: During the exploratory phase 2012–2018), we clinically tested the usefulness of the ‘SENS’-structure for initial PC assessment, organization of the interprofessional care plan, documentation, and evaluation of patient preferences and priorities. Over a two-year period, and as part of the PC national audit process , profess12–2018, In addition, in October/November 2018, we performed an online survey among clinical users of the ‘SENS’-structure in the German speaking part of Switzerland in various settings  to collect formal and anonymized feedback from professionals. The local ethical committee of Bern considered this survey as being outside their sphere of responsibility. Participants  were identified and approached by our two major PC centers , providing a link by email to the respective survey. Their anonymous participation was considered as consent to participate in the survey.In phase 3, we performed a randomized controlled ‘SENS’-study  . Within this article, we focused only on the developmental and explorative phases and will discuss the findings of Phase 3 and 4 in later publications.As major thematic structures for anticipatory planning in PC, we identified the definition of the World Health Organization (WHO) on PC ,24, the Symptoms patients suffer from or worries they may have, including self-support strategies in case of a crisis, as well as carer empowerment in symptom crises;End-of-life decisions in regard to the future, including individual care and treatment preferences, potentially formulated as an advance directive;Network organization , including assessment of current living circumstances and organization of support in case of an emergency not manageable at home/nursing home;Support for the carers to cope with the situation and to prevent overburdening.By analysis of the content of consultations, we identified four main clusters of themes that we regrouped to form the ‘SENS’-structure. The acronym ‘SENS’ represents the following themes :SymptomsWe compared the goals of care identified in the preclinical phase with the ‘SENS’-structure and saw that ‘SENS’ seems to covers all relevant themes. Subsequently, we used ‘SENS’ to design a patient prompt sheet with concrete questions to assist patients and families in preparing for ePC conversations with health professionals .Together with other Swiss healthcare facilities, we evaluated the usefulness of ‘SENS’ in the acute hospital and outpatient PC setting.A formal evaluation among professionals revealed important feedback. The majority (49.1%) of the respondents had a nursing background , workingOverall, ‘SENS’ was used by the respondents both in the early PC phase and in the dying phase, respectively (72.2%). Respondents used ‘SENS’ mainly within the interprofessional team context, and it was rated most helpful for providing an overview of current problems and individual expectations, as well as for interprofessional collaboration and coordination. The vast majority of the respondents (81.5%) felt that the active participation of patients and family carers in defining a common care plan was well supported by the ‘SENS’-structure. Although professionals need certain training to lead the conversation with the patient according to the ‘SENS’-structure, most participants (87%) had good experience with the use of ‘SENS’. Specifically, when patients do not want to address the issue of dying and death, some professionals felt that it was challenging to use ‘SENS’ with all its components.Feedback from professionals (nurses and physicians) and patients as part of annual medical quality assurance audits, as well as responses from the survey, revealed benefits in the following four main areas:Firstly, the ‘SENS’-structured initial assessment seemed to help patients and families to gain a systematic overview of future challenges. ‘SENS’ encouraged them to talk about these—often very personal—worries. Often, a medical diagnosis-driven approach was in the foreground; therefore, some patients seemed challenged by focusing early on non-medical topics and analyzing their situation systematically.N” = network  and the second “S” = support for the carers was not always clear. We concluded that “support” as term alone may be misinterpreted and added “for the carers” to specifically focus on the assessment of carer or family burden.We found that all four areas of ‘SENS’ were equally relevant. ‘SENS’ provided an initial overview which could then be assessed in greater detail using validated assessment tools for specific symptoms whenever appropriate. The structure seemed to be evenly helpful in ePC, in more advanced and complex palliative situations and the dying phase. The distinction between managing symptoms and discussing end-of-life decisions was obvious to clinicians. In some documented conversations, the difference between the “These findings were supported by the results of the online survey, in which the majority of the participants felt that all four areas covered always or almost always the situation of patients. This was particularly clear in the themes ‘Symptom management’ (94.6%), ‘End-of-life decisions’ (89.3%), and ‘Network-organization’ (78.6%). Probably the theme ‘Support for the carer’ still needs more refinement since 50.9% of the survey participants felt that it covered the support for patients but not the support needs for the burdened carers. It offered a needs-based approach for multifaceted situations driven by concrete problems of daily life, while utilizing resources of patients.From the perspective of health care professionals, ‘SENS’ has been independently used and proven to be helpful in clinical practice  for struSecondly, from clinical practice, we know that ‘SENS’ was often used in interprofessional rounds. It showed that ‘SENS’ served as a useful structure to develop a care plan together with patients, guiding the setting of goals of care, expectations, prioritization, responsibility, planning, and allocation of resources and financial reimbursement. It also supported the collaboration within the interprofessional team , as shown in postgraduate basic and specialist PC training programs in Switzerland [tzerland . At the tzerland  as recomtzerland .In phases 3 and 4, we are currently investigating the impact of a ‘SENS’-structured conversation on patients’ distress and trust through two RCT. In a prospective RCT including an embedded qualitative study with ePC cancer patients  , we inveThe ‘SENS’-structure was developed using a stepwise approach for complex interventions, as recommended by the MRC . Most ofIn terms of completeness compared to other assessment structures in PC, from the health care professionals’ perspective, ‘SENS’ seemed to covers all relevant themes. Clinical audits showed that ‘SENS’ seemed to support patients and carers to be an active part in generating anticipatory planning and to feel empowered for future challenges. In addition, it seemed to enable interprofessional teams to prioritize and focus, together with patients and families, on what is important and manageable in individual situations.Despite complex medical situations, ‘SENS’ seemed to help patients to regain control over personal areas of life and to shift the focus to patients’ strengths and resilience, facilitating their self-efficacy. More research on this effect has been undertaken. Thus, ‘SENS’ is more likely to contribute to a flexible and equally comprehensive concept of biopsychosocial and spiritual care, alongside diagnosis-based medical reasoning and disease-specific approaches, as recently proposed through the term “concurrent care” by the American Society for Clinical Oncology (ASCO) ,41. ConcDuring the different phases of developing and piloting ‘SENS’, we encountered clinical acceptance by professionals when ‘SENS’ was administered alongside medical reasoning.No harmful effects of the ‘SENS’-structure have been mentioned in any feedback from professionals, but more detailed evaluation is underway. Some professionals would expect that patients could experience the systematic analysis of ‘SENS’ themes as challenging, perhaps because patients might not be prepared to deal with the life-limiting nature of their disease or to discuss PC issues early on in their disease trajectory. This could especially be true in a health system with a clear focus on “cure”, with healthcare providers being less likely to actively and early address PC needs. ‘SENS’ may help to structure communication on anticipatory planning complementary to medical reasoning. Health professionals may finally feel better prepared and trained for end-of-life conversations. Such conversation has earlier been entitled ‘the multimillion dollar conversation’ because of its ability to save money through the redirection of hope and trust from potential toxic medical intervention to individual goals of care .The current Swiss healthcare system is a strongly diagnosis-oriented system (DRG), while PC is a problem-based approach. Defining specific activities for PC, therefore, is paramount to obtain sufficient financial reimbursement. The ‘SENS’-structure has recently been officially acknowledged as the basic assessment-structure triggering the comprehensive DRG-codes for complex palliative treatment . IntroduThe ‘SENS’-structure has been integrated into professional PC curricula at undergraduate and postgraduate levels in our country . The fouAlthough qualitative and health service research is getting more attention, areas such as decision-making, health care services/network, and areas of burden and resources of the family are less frequently a subject of interprofessional research. ‘SENS’ may also allow for a more comprehensive view on palliative research topics and on their weighting.Further development and refinement of the ‘SENS’-structure and testing in clinical and educational settings through research is needed. Whether ‘SENS’ truly covers all aspects of assessment and care planning in PC for specific populations, such as pediatric, elderly, and patients with dementia, remains to be determined by systematic research.Since the medical field is mostly aligned with a diagnosis-focused approach, a problem and resources-oriented approach is rather unusual. ‘Concurrent care’ has recently become a promising model of care to bring these two approaches together. The ‘SENS’-structure was developed to foster a strong patient-needs-based and participatory approach in such a model.The starting point of our search for a meaningful person-centered interprofessional PC approach led us toward a structure that is easy to use in clinical practice in and across all settings, but also in PC research and professional education. Support from a specialized PC team might encourage and facilitate the implementation of the ‘SENS’-structure into care pathways for patients with a life-limiting disease.The ‘SENS’-structure seemed to covers all themes and complexity as proposed by other structures, such as the GSF or the NCCN guidelines for PC. Providing guidance for ePC conversations, including ACP, it focuses particularly on patient perspectives. Practice experience so far shows that ‘SENS’ helped patients and their families facing complex PC situations to plan proactively and to be prepared for future challenges, including death and dying. In addition, professionals reported its usefulness to prioritize and focus on what is important and manageable in the individual situation and to define task distributions within a common care plan through direct cooperation and mutual respect. Further research to confirm the usefulness of the ‘SENS’-structure as experienced by patients and clinicians is required in phases 3 and 4 of the MRC framework.In the German-speaking part of Switzerland, the ‘SENS’-structure has been implemented in clinical practice in hospitals, as well as in primary care over the past six years and has been recommended by the Swiss federal office of health. We anticipate that the ‘SENS’-structure may play a significant role in further development of ‘concurrent’ treatment, combining disease-modifying treatment and a person-centered care plan, making sense for patients’ family, and carers, as well as for professionals.


Junta para Ampliación de Estudios (JAE). The JAE was an organism set up to help promising researchers develop their careers in different ways, thereby contributing to the development of science in Spain. Although largely forgotten or relatively unknown, there has been a recent revival in the recognition of the school that developed around Cajal, collectively referred to as the Spanish Neurological School . Almost all Cajal’s collaborators were men, although a limited number of female scientists spent part of their careers at the heart of the Cajal School. Here we discuss these women and their work in the laboratory in Madrid. We have tracked the careers of Laura Forster , Manuela Serra, María Soledad Ruiz-Capillas and María Luisa Herreros , through their scientific publications, both in the journal founded by Cajal and elsewhere, and from other documentary sources. To complete the picture, we also outline the careers of other secondary figures that contributed to the production and running of Cajal’s laboratory in Madrid. We show here that the dawn of Spanish neuroscience included a number of contributions from female researchers who to date, have received little recognition.At the beginning of the 20th century, in view of the growing international recognition of Santiago Ramón y Cajal, the Spanish authorities took some important steps to support Cajal’s scientific work. This recognition peaked in 1906, when Camillo Golgi and Santiago Ramón y Cajal shared the Nobel Prize in Physiology or Medicine. The Spanish government provided Cajal a state-of-the-art laboratory in Madrid to allow him to continue with his research and they funded salaries to pay his first tenured collaborators, the number of which increased further after the creation of the  In a historically brief but intense period, Cajal studied almost all the structures of the nervous system in different species and his productivity at that time is still difficult to conceive. Yet most importantly, through these studies he formulated the so-called “neuron doctrineneurons” . At the neurons” ,b. The pneurons” ,b.Junta para Ampliación de Estudios-JAE (Council for Extended Studies), the presidency of which was entrusted almost immediately to Cajal himself  incorporated neuropathology as a new research line in Cajal’s laboratory; Pío Río-Hortega (1882–1945) identified two of the four classic cell types that make up the CNS (oligodendrocytes and microglia); Fernando de Castro (1896–1968) unraveled the innervation of the carotid region and identified the first chemoreceptors in the carotid body; and Rafael Lorente de Nó (1902–1990), among other achievements, described the organization of the audio-vestibular system and he was the first to suggest the columnar organization of the brain , he listed all the members of the School , and the Prime Minister, Francisco Silvela (1843–1905), convinced the Spanish government to establish and fully furbish a modern Histology laboratory for Cajal in 1901 ,b. The f himself . The JAEmanities . Some ofhe brain . Togethee School , which ie School . These wboratory . We haveNew South Wales Parliament from 1856 to 1880 and Premier of New South Wales during 1850–1860, subsequently occupying different portfolios. Her mother died when she was a little girl (1862) and her father then married Maud Edwards, adding five more children to the family. When Mr. Forster died (1882), Laura moved to England in the company of her stepmother and one of her half-sisters. Initially educated in Australia, in 1887 Laura Forster entered the University of Bern (Switzerland) as a medical student, receiving her M.D. in 1894. There, she worked for 6 years at the Institute of Pathology, devoting her research to the study of muscle spindle fibers. She later published her first scientific paper on these structures when in Oxford, focusing on their development in human fetuses between 4 and 6 months of gestation  to practice medicine. In 1900, she was appointed medical officer at the stology” , “for hirculosis .Tulane University  as Professor of Physiology in 1908, together with the recent international prizes awarded to Santiago Ramón y Cajal between 1900 and 1906, prompted Laura Forster to spend time in Cajal’s laboratory to gain a greater command of neurohistological techniques. According to Cajal and Forster, the latter worked for “a few months” in 1911 at the Laboratorio de Investigaciones Biológicas or Cajal’s laboratory  and the fact that he moved to boratory ; Table 1boratory . In facts work5” . We shoueriology . Cajal ceriology , 1917. Aeriology .British Red Cross and worked at the British Field Hospital in Antwerp (Belgium), becoming the first female Australian doctor to assist in the wartime medical effort, although as a woman, she was again not allowed to enlist in the Allied Medical Corps. After a short time working in Northern France Laura was sent to Russia where she volunteered as a surgeon at the largest hospital in Petrograd . She remained there for several months after the Autumn of 1915, working “very happily with the Russian doctors, without need of an interpreter” . She then joined the Russian Red Cross and served in the Caucasus and Erzurum (Turkey), supervising a 150 bed, infectious diseases campaign hospital in the middle of a typhus epidemic during the summer of 1916. Her final destination was a hospital in Zalishchyky, in the Galicia region (Russia), just 30 miles away from the front and attached first to the 9th and after to the 7th Army . That was one of the five hospitals in the region operated by the National Union of Women’s Suffrage Societies (United Kingdom), where thousands of soldiers and especially civilian refugees were treated for typhoid, scarlet fever, dysentery and different types of war wounds and traumatic lesions. The exhausting work, frequent bombardments and the exposure to infectious sick people seriously affected the health of Laura Forster and she died on February 11th, 1917, and was buried in Zalishchyky under Russian Orthodox rites.The career of Laura Forster underwent a drastic turn in 1912 and when the First Balkan War was declared, she traveled to Epirus to enlist as a nurse, since women couldn’t serve as physicians at the war front. From that moment onward, the life of Laura Forster is linked to war. Immediately after the outbreak of the Ist World War she joined the Royal Society the last of Forster’s findings from her work at the Pathology Laboratory at the Claybury Asylum , all collected at the asylum, at the Charing Cross Hospital and London Hospital , and at the London County Asylums  in the white matter and possibly, the gray matter. Serra used the “Cajal’s new method to color neuroglia” , later at the Instituto Cardenal Cisneros (Madrid) . Having successfully passed all the exams in the first 3 years of her degree in medicine with high grades, she was the best of the 73 aspirants that applied for the position as “alumno interno” at the Beneficencia Provincial (Madrid), which was therefore offered to her , financed by the JAE. Between 1928 and 1930, María Soledad Ruiz-Capillas worked under the direction of Gonzalo R. Lafora and his assistant Julián Sanz-Ibáñez, studying the neural centers involved in sleep pathologies  , and in d to her . This wad to her . In 1928hologies . Specifihologies . Sleep phologies . Dr. Ruigotten…” . During gotten…” . In the Instituto Cajal moved from the building at No. 13 Paseo de Atocha to a new site at San Blas hill, within the Retiro Park. This initially advantageous change generated a problem of incompatibility between the new electrical installations, working from AC, and most of the scientific equipment used in the old building that worked on DC. This problem meant electrical drills, microphotography systems, exploration and surgery lamps, etc., could no longer be used and therefore, there was a substantial delay in the progress of the research into diencephalic and mesencephalic physiology , as well as a post as director of the Grávalos spa (La Rioja), from where she sent her letter of condolences upon the death of Santiago Ramón y Cajal that is conserved in the Legado Cajal  announced that on Monday 13th at 19:00 “Doctors Lafora and Sanz will present their personal experiences on the neural sleep centres, with fixed and cinematographic projections” at the Spanish Medical Chirurgical Academy . Given the transcendental local elections celebrated the Sunday April 12th, prior to the presentation and their consequences  , and sheUniversidad Central de Madrid (now the Universidad Complutense), which was relatively exceptional for Spanish women at that time9. She lived at the Residencia de Señoritas (see above), an institution founded by the JAE in 1915, 5 years after its male counterpart. This unique institution was founded thanks to generous support from the United States to promote higher education among Spanish women (involving the Boston Committee and an active exchange program with Smith College). Indeed, the Residencia de Señoritas occupied the former International Institute for Girls in Spain, owned by the United States government  on October 3rd, 1917, where her father was the owner of a business dedicated to carpentry and marble stonemasonry, while her mother owned a sewing business. These wealthy roots and her extrovert personality, led María Luisa’s parents to school her under local French Nuns, allowing her to complete her bachelor studies in Torrelavega. In 1934, María Luisa Herreros moved to Madrid to study at the Medical School of the vernment . The Resillance” .Residencia, María de Maeztu Whitney (1882–1948), was an important educator and feminist activist from a well-known intellectual family. Her brother Ramiro was a right-wing reformist thinker, writer, journalist and diplomat, and her other brother, Gustavo, was a painter. The international element in the Maeztu family came from her father, a civil engineer born in Spanish Cuba (see above), where he worked and married the daughter of a British diplomat. In the internal files of the Residencia de Señoritas, María Luisa Herreros was reportedly extremely interested in her classes, and she was open and nice. Maria Luisa learnt Histology and Pathology from Prof. Jorge Francisco Tello, not only the first true disciple of Cajal but also, his successor as university chair and as director of the Instituto Cajal until 1939. Her Physiology professor was Juan Negrín (1892–1956), who became the last prime minister of the IInd Spanish Republic (1937–1939). Both these professors gave María Luisa Herreros the highest grades, as seen in the academic records from the Universidad Central, Madrid, and in those of the Residencia de Señoritas.The Director of the But the political situation in Madrid at the end of the 1935–1936 academic course was very tense, with the frequent shooting of activists on the far-left and far-right. As a result, María Luisa decided to go back home, close to the Cantabrian seaside, which is where she was at the outbreak of the Spanish Civil War. The province of Santander (currently the autonomic region of Cantabria) was initially part of the area controlled by the legal Republican government. María went to work at the military hospital that opened in Torrelavega and she also helped perform autopsies on those who were assassinated by being thrown from the Santander lighthouse into the wild sea. The war ended for her in August 1937, when the rebel troops of general Franco occupied this Northern region.Residencia de Señoritas, now transformed into the Colegio Mayor Teresa de Cepeda and later, into the Colegio Mayor femenino Santa Teresa de Jesús due to the dissolution of the JAE by Franco. Although the general and political situation in Spain had changed, the new Director of the Colegio Mayor Santa Teresa de Jesús was Matilde Marquina, who tried to continue ensuring that women had access to higher education, adding “special attention to the religious and moral education of the residents” that included daily religious services in the chapel .When the Spanish Civil War ended (1939), María Luisa Herreros returned to Madrid to continue her studies in Medicine. She again lived in the pavilions of the former e chapel . As can Instituto Cajal with Fernando de Castro (see above), one of the few researchers from the Spanish Neurological School who remained in Madrid throughout the Spanish Civil War, defending the building together with Tello ] to implement psychoanalysis in Spain were halted abruptly by the outbreak of the Spanish Civil War. Indeed, the authoritarian regime established in Spain after the end of the Civil War was firmly based on rigid traditionalism that admitted no discrepancies, inculcating religious and military values that were not propitious for the development of psychotherapies. Nevertheless, Jerónimo Molina Núñez, a disciple of Garma, tried to keep the psychoanalytical flame alight and he found a way for Margarita Steinbach  to come to Spain in 1950. Steinbach worked actively in setting up the first group of psychoanalysts in Madrid and she submitted María Luisa Herreros to a psychoanalysis. Subsequently, Molina Núñez, Ramón de Portillo, María Teresa Ruiz and María Luisa Herreros founded the Asociación Española de Psicoanálisis-(AEP), officially approved and registered in 1954. In order to become recognized by the IPA, a number of psychoanalysts from Madrid and Barcelona attended a meeting in London (United Kingdom), where María Luisa Herreros and Teresa Ruiz met Anna Freud (the daughter of Sigmund Freud). Mrs. Freud invited these Spaniards to tea at her own home and she advised them how to get the embryonic Spanish association (AEP) accepted as a member of IPA. In July 1957, at the 20th meeting of the IPA, it was agreed to assess the Hispano-Portuguese group of psychoanalysis and finally, the Sociedad Española de Psicoanálisis (SEP) was accepted as a formal member of the IPA in 1959. From the very moment that she became interested in psychoanalysis, Dr. Herreros remained strongly influenced by Freud, although she never occulted her fascination for Carl-Gustav Jung As such, Herrero’s therapeutic approaches could not be circumscribed to a single school.Dr. Herreros became interested in psychoanalysis from her beginnings as a psychiatrist, even though, for different reasons, this therapeutic approach was not very popular among medical professionals in Spain. Indeed, and perhaps most importantly, Santiago Ramón y Cajal had a very strong opinion of psychoanalysis and in the words of José Lázaro, Cajal thought that e brain” . In addiDr. Herrero’s career as a clinical psychoanalyst was intense and extensive, publishing the chapter entitled “Norms for Psychotherapy” in a very famous textbook by Prof Juan Rof-Carballo . In 1973Psique In 1976, an association for research into and the application of psychoanalytic therapy, and mainly, to train new generations of psychoanalysts. However, the project was cut short by the Hogdkin’s lymphoma that caused María Luisa Herreros’ death in Madrid on October 3rd, 1985. This was the second death within the Spanish Neurological School due to Hodgkin’s lymphoma, which also caused the premature death of one of Cajal’s direct disciples in 1918, Nicolás Achúcarro , Dr. Herreos funded chúcarro . In SpaiAlthough chronologically this section represents a step backward, this work would not be complete without mentioning and briefly analyzing the role of the women illustrators that collaborated with the Spanish Neurological School. Although microphotography began to be used in Cajal’s lab at the beginning of the 1920s, it could not compete with the quality and quantity of information gained from the histological drawings of the stained material. This was usual at the time until new staining procedures were developed that generated less background, and the situation also changed dramatically in the 1970s with the development of fluorochromes, and of antibodies conjugated with these for immunohistochemistry and immunocytochemistry. However, it is true that after the II World War, scientific photography became more and more common and manual drawing was phased out from laboratories, and hence, from the scientific literature. But drawing by hand was undoubtedly the perfect complement to the Golgi method and to the other simple techniques on which modern Neuroscience was founded. Besides being ground-breaking scientists, Cajal and some of his disciples, like his brother Pedro, Domingo Sánchez, Achúcarro, del Río-Hortega, de Castro and Lorente de Nó, were true masters of this art . Yet cur14 , we conclude that: 84 of these were signed by “Del Valle” or “C. del Valle,” referring to Conchita del Valle; 71 by “15Legado Cajal, where 8 photomechanical prints (printing proofs) indicate from “drawings of C. del Valle from microphotographs,” although only two of them are signed by “C. del Valle”. While it was Francisco Tello who most specifically needed this technical assistance, the illustrators eventually collaborated with other researchers as well. One of the most distinguished neuroscientist in the laboratory, Fernando de Castro, despite his own talent as an artist, once requested the collaboration of Conchita del Valle to specifically take advantage of her talent as an illustrator to depict one of the terminals at the carotid sinus after a 12 day ablation of the sympathetic trunk. This was a polychrome image used in de Castro’s first paper published after the Spanish Civil War, and once Heymans had been awarded the Nobel Prize , substituting her sister, Irene Falcón17, when she moved to London in 1926. Both sisters were born in Madrid to a Polish middle-class businessman, Siegfried Lewy, and they were raised as German speakers before Mr. Lewy abandoned his family. This aspect of Ketty’s education was useful for Cajal and other researchers to translate works published in German, and especially, to write letters to and communicate with German-speaking scientists. Yet Mrs. Lewy was a very secondary actor in Cajal’s circle , the former librarian had no problems to return to the Spain of Franco in 1971 and she was hired by the Spanish Research Council (Consejo Superior de Investigaciones Científicas-CSIC), the public administration that Franco founded in 1939 to absorb the Instituto Cajal and to take on the other responsibilities of the JAE. There, she worked in the Scientific Documentation Service and she collaborated with the journal Arbor, published by the CSIC . True experts consider this book more a kind of “auto-hagiography,” full of imprecisions and errors. For example, the reference to the death of Cajal clearly clashed with the published press reports and multiple testimonies from those who were present when Santiago Ramón y Cajal died . The diffusion of Lewy’s book  in the list of his School depicted in 1922 , and incInstituto Cajal, the Spanish Research Council (JAE Archives) nor in the University archives. Some experts confused her with her sister, Mrs. Carmen Serra, who was also a laboratory assistant  who worked for years at the Instituto Cajal, perhaps better known to experts in Cajal’s circle due to her presence in different photographs , for decades both leading the work of the so-called “brain localizationists” in Germany ; the Russian-born Marie Nageotte-Wilbouchewitch (1864–1941), M.D. from the Université de Paris (France), who collaborated with her husband Jean Nageotte on the study of neuroanatomy and different CNS pathologies, a recognized Pediatrician who became the first president (of any gender) of the Societé Française de Pédiatrie; the Romanian-born French psychiatrist Constanza Pascal (1877–1937), specialist and pioneer in dementia and dementia praecox; the German medical doctor Martha Ulrich (1881–1943), maybe the first woman to ever publish an article on glial cells; and undoubtedly the most influential and well-known, although corresponding to a later generation, Rita Levi-Montalcini (1909–2012), a graduate in Giuseppe Levi’s laboratory when Fernando de Castro worked at Torino in 1934, who obtained the Nobel Prize in 1986 with Stanley Cohen after decades of international recognition for the discovery of the Nerve Growth Factor , who worked with Ivan Tarkhanov on sleep research and pioneered studies into the effects of sleep deprivation on animals, and Lina Solomonovna Shtern (1878–1968), one of the pioneers on the blood-brain barrier; the Polish-born Micheline Stefanowska (1855–1942), who worked in Switzerland, Belgium and France, studying a variety of issues from pain psycho-physiology to dendritic spines – she described the plasticity of dendritic spines after electric stimulation, a hypothesis that Cajal, who first described these structures in 1888, considered plausible but not fully demonstrated at that time; the American-born French clinician Augusta Marie Déjerine-Klumpke (1859–1927), first woman to work as an intern in a hospital in Paris and who described Klumpke palsy caused by damage to the peripheral nerves controlling arm movements, author of about 60 papers and co-author with her husband, Joseph Jules Déjérine, (1849–1917) of the two-volume book “Anatomie des Centres Nerveux,” both disciples of Vulpian; the French Cécile Vogt , disciple of Pierre Marie, who devoted special attention to the study of myelination and the white matter of the brain, and who was as relevant as her husbandh Factor . Neverthh Factor . Interesh Factor .Although female researchers were little known within the School of Cajal, the laboratory was open to accepting women and not only as secondary collaborators  but also as independent researchers, to the extent of including two of them in his own description of the school in 1922 . We hopeEG proposed the study. FdC, CN, and EG performed the research. FdC, CN, and EG wrote the manuscript. FdC prepared the figures. EG, CN, CM, and CS corrected the manuscript and contributed with figures for some panels.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling Editor and reviewer J-GB declared their involvement as co-editors in the Research Topic and confirm the absence of any other collaboration.


Although personality and cognitive ability are separate (sets of) constructs, we argue and demonstrate in this article that their effects are difficult to tease apart, because personality affects performance on cognitive tests and cognitive ability affects item responses on personality assessments. Cognitive ability is typically measured with tests of items with correct answers; personality is typically measured with rating-scale self-reports. Oftentimes conclusions regarding the personality–ability relationship have as much to do with measurement methods as with construct similarities and differences. In this article, we review key issues that touch on the relationship between cognitive ability and personality. These include the construct-method distinction, sources of test score variance, the maximal vs. typical performance distinction, and the special role for motivation in low-stakes testing. We review a general response model for cognitive and personality tests that recognizes those sources of test score variance. We then review approaches for measuring personality through performance , test and survey behavior , and real-world behavior . We also discuss ability effects on personality tests, indicated by age and cognitive ability effects, anchoring vignette rating errors, and instructions to ‘fake good’. We conclude with a discussion of the implications for our understanding of personality and ability differences, and suggestions for integrating the fields. This article reviews evidence for how cognitive ability and personality traits are integrated. There is a substantial literature that examines the correlations between measures of cognitive ability and measures of intelligence, contemporaneously ,2, and lWe do not review that literature.The article is organized as follows. First, we review the construct-method distinction—the distinction between cognitive ability and personality constructs (or ‘traits’), and the methods used to measure those constructs. We believe these are almost always confounded, and often conflated, as indicated in, for example, the ‘personality change’ literature, which deals almost exclusively with changes in responses to a very specific kind of assessment, a self-rating Likert scale, rather than to personality per se ,11. We aFollowing our review of personality determinants of performance on cognitive measures, we review instances of cognitive influences on traditional personality tests. These include age and cognitive ability effects, anchoring vignette rating errors, and ability to follow instructions to ‘fake good’ on personality tests. We conclude with a discussion of the implications for our understanding of personality and ability differences and suggestions for integrating the fields.To answer the question, “what is the relationship between personality and intelligence?” it is helpful to start with definitions. A suitable definition of personality comes from the American Psychological Association (APA), as follows: “individual differences in characteristic patterns of thinking, feeling, and behaving”.3For a combination of historical, accidental, and practical reasons, two broad approaches to measuring these two constructs have emerged and come to dominate how we think of them. For personality, the dominant methodology has to do with endorsements of descriptions of characteristic behavior, thoughts, beliefs, or attitudes. Descriptions can be trait terms or statements; endorsements can be ratings, rankings, or preference judgments between two or more descriptions; and the endorsements can be done by the self or by others—peers, teachers, or supervisors. There is a lively body literature in personality psychology on the differences between these methods, but personality is most often measured with self or peer ratings of statements using the Likert scale format . The essFor intelligence, the dominant method is the standardized test, with a problem and response format , scored as right or wrong, or in some cases, partially right. This characterizes all IQ, achievement, and selection and admissions tests such as the Armed Services Vocational Aptitude Battery (ASVAB) and the College Board’s SAT test.So, the answer to the question of “what is the relationship between intelligence and personality?” would typically be given by a correlation between a measure of personality using the rating scale method, and a measure of intelligence based on a test. From this literature we find that those who score well on intelligence tests are slightly more likely to say that they “enjoy hearing new ideas” compared to those who score poorly, but are equally likely as their low scoring counterparts to say that they “respect others”, “pay attention to details”, “make friends easily”, or “worry about things”. Studies such as this abound in the literature ,3,16, vaHowever, intelligence can just as easily as personality be evaluated with the statement endorsement methodology. That is, rather than giving a test, we can ask examinees their level of agreement to statements such as “I understand complex ideas”, “I adapt effectively to the environment”, “I learn from experience”, or “I engage in various forms of reasoning to overcome obstacles by taking thought”. This is not the typical way we measure intelligence, and in fact, personality psychologists might recognize or characterize some of these statements  as measures of the ‘intellect’ facet of openness. But our point here is to distinguish the constructs  from the methods used to measure them .Conversely, personality can be measured with tests. This is arguably more challenging than measuring intelligence with ratings, and there is not a consensus literature on how to do this. In this article, we review a number of different approaches for measuring personality without ratings.The points here seem obvious, but we believe that there is a tendency to ignore the construct-method distinction when discussing personality and intelligence. We hear too often discussions about personality that really are discussions about examinees’ responses to Likert rating scales of personal descriptions. Similar to Boring’s  oft-repeCronbach , followiSome of the cognitive test score variance sources are marked as personality  or as a state source . We do this both to highlight the importance of personality and mood-state variance sources in cognitive testing, which is the main point of this article, but also to show that the role of these other factors has been acknowledged in psychological testing for a long time. We also provide mostly contemporary references for these factors that illustrate how they contribute to performance on tests. In this article, we examine a number of these alternative variance sources in depth.There has been longstanding awareness of the potential for personality and mood to enter into cognitive test performance. However, the major effect of that awareness has been to be mindful of these potential sources of contamination in test design (third column), and occasionally, for particular studies to model the alternative variance sources (fourth column). For the most part, however, we argue that these potentially confounding influences are ignored.Borghans, Duckworth, Heckman, and ter Weel  provide They also explicitly proposed that the measured traits , incentives, and other influences on test scores. For example, generalizability theory ,49 speciHeckman and Kautz  providedPersonality traits and intelligence are normally conceptualized in different ways. Personality traits are often defined in terms of typicality—stable patterns of behavior over an extended period of time ,56,57. IIntelligence is implicitly (and sometimes explicitly) conceptualized and measured as what people are able to do and is defined as the limit of a person’s intellectual repertoire, which can be expressed when that person is exerting maximum effort ,67. CognJust as people are capable of expressing personality traits to greater (or lesser) extents than they ordinarily do, people are also not usually motivated to express the utmost limit of their intellectual skills on an everyday basis. Consequently, there is no guarantee that individuals will demonstrate the full extent of their intelligence across the situations they encounter in their daily lives . For example, a person with a Ph.D. in engineering (or in English literature) will be capable of solving highly complex mathematical problems (or writing a thought-provoking essay), but may not feel motivated to do so if those problems (or essay prompts) are presented under low-stakes conditions without adequate incentives. Although the extent to which individuals demonstrate their intelligence in everyday situations has been explicitly studied in terms of dispositions , typicalsamples—actual performances that directly demonstrate the construct [signs—indirect indicators of the constructs of interest [r = 0.29 to r = 0.41 [7Because intelligence is treated as the upper limit of individuals’ cognitive skills, it is amenable to being directly measured, as it is not necessary that people maintain the expression of this upper limit beyond a relatively short period of time . Intelligence tests can thus be conceptualized as onstruct ,74.6 In onstruct ,80 and tinterest ,74. Thesr = 0.41 .7r = 0.42 [The meta-analytic correlation between typical and maximal performance in the workplace has been estimated to be r = 0.42 . BehavioThere is a corollary to the fact that many people do not enact the full extent of their cognitive skills on an everyday basis because they lack the incentive to do so: In testing situations where the stakes are low  many test-takers also lack the incentive to exert the effort necessary to perform as well as possible. Although the potential for high-stakes conditions to lead examinees to distort their responses to personality measures has been noted for decades  less attThe implications of differences in motivation for the construct validity of intelligence tests administered under low-stakes conditions have occasionally been explored over the past 70 years ,90,91,92Being dispositionally motivated to achieve is related to task persistence and engagement , stronglThat a substantial portion of the variance in cognitive test scores may be attributable to personality in low-stakes settings but not high-stakes settings implies that assessment conditions (and incentives) may be an important moderator of observed associations between personality and intelligence. For instance, given that conscientious people are more likely to exert effort in general, it might be expected that the correlation between conscientiousness and intelligence test scores will be higher in non-incentivized, low-stakes conditions than in high-stakes conditions. A cursory review of studies reporting correlations between conscientiousness and ACT/SAT scores, and conscientiousness and low-stakes test scores in different samples supports this hypothesis in a preliminary way. Richardson, Abraham, and Bond’s  meta-anaCattell and Warburton  distinguEuropean Journal of Psychological Assessment [A more recent treatment was provided in a special issue on Objective Personality Tests in the sessment . Ortner sessment  providedsessment  conditioA second category is OPTs that represent real-life situations, particularly risk propensity. An example is the Balloon Analogue Risk Task , in whiThe third category is questionnaire-type OPTs that ask for decisions. An example is one in which a problem is presented , which is scored for assertiveness/confidence as the latency to respond, regardless of the response.9d = 0.28) in performance on a standardized test. This study illustrates a couple of principles. One is that the relationship between personality  and intelligence (performance on the math test) is not fixed, but it can be modified by an intervention focusing on beliefs about the importance of effort. Second, it is possible to measure a personality construct by way of a decision behavior related to a game-like task.Alan, Boneva, and Ertac  evaluateSegal  argued tAlmlund, Duckworth, Heckman, and Kautz . A negative reciprocity measure determined what the minimal amount of money a respondent would accept from another would be, before rejecting the offer . Next, numerous survey items were administered to the same respondents, and regression analyses were used to select the two survey items that best predicted performance on the experimental measures. The survey items were of two types, qualitative and quantitative. Qualitative items were typical Likert style personality items, such as “Are you a person who is generally willing to take risks, or do you try to avoid taking risks?” (risk-taking), “How willing are you to give up something that is beneficial for you today in order to benefit more from that in the future?” (time preference), “As long as I am not convinced otherwise, I assume that people only have the best intentions” (trust), and “Are you a person who is generally willing to punish unfair behavior even if this is costly?” (negative reciprocity). The quantitative measures were typically survey versions of the experimental measure, which asked respondents what they would do in a situation. For example, a respondent would be given a set of choices of payments, they would prefer “today” vs. “in 12 months”  (time preference). Or they would be told “Suppose you won a lottery for $1000, how much would you give to charity?” .Falk et al.  estimater = 0.43; [In a series of studies, Stankov and colleagues ,131,132  = 0.43; . There i = 0.43; , which s = 0.43; .The effort one puts forward in responding to a survey, both by returning it, and by completing all items on that survey, might be understood as an indicator of either a temporary or lasting characteristic of that individual ,137,138.A question is whether lack of effort, indicated by skipping background questions, is related to conscientiousness or other Big 5 personality factors, measured by surveys. Zamarro, Cheng, Shakeel, and Hitt  examinedBesides careless answering, returning the survey itself may be taken as an indication of effort. Cheng, Zamarro, and Orriens  examinedIt is commonly assumed that an item’s difficulty  is not affected by whether that item is administered early or late in a test . That assumption is sometimes relaxed, in acknowledgement of warm-up and fatigue effects, which may increase item difficulty . A commoN = 93,000+). He found similar effects for the Graduate Record Exam (GRE) (N = 5000+). In both studies there was item heterogeneity in the sense that items varied in their susceptibility to position effects; on the GRE the range was from a proportion correct decreasing by 0.17 to increasing by 0.03 in early vs. late item positions. One question concerns the severity of item position effects. This seems to vary depending on circumstances, but there certainly is considerable evidence for them. Albano  found thN = 460,000+, 65 countries), using an item-response theory (IRT) model that included item position effects, which enabled modeling test-taker effort . They found both a general decrease in effort across countries, and large individual, school-level, and country-level differences in the decrease of examinee effort over the course of the test. The amount of decrease was associated with the overall performance level. For example, students from Finland, which is a high (average) performing country, showed a relatively small decrease in effort (d = −0.09), whereas students from Greece, which is a lower performing country, on average, showed a larger decrease in effort over the course of the test (d = −0.28). There were also school effects on persistence, supporting a positive relationship between persistence and ability . The large lesson here is that differences in PISA scores between countries at least partly reflect noncognitive (persistence) differences between students in those countries. This result was replicated at the student level in a large-scale German achievement study [Another question is whether there are group or individual differences in susceptibility to the item position effects. Debeer, Buchholz, Hartig, and Janssen  , was only modestly related to a self-report prompt, “I procrastinate” with a 0–10 response scale (r = −0.22), which itself had a lower correlation with the number of exams passed.Richardson, Abraham, and Bond  identifiThat personality does not have to be measured by Likert scale self-reports has been explored in various studies. Fast and Funder  examinedr = 0.75), size of Facebook friendship network (r = 0.47), openness (r = 0.43), extraversion (r = 0.40), and intelligence (r = 0.39), were fairly well predicted; for the categorical variables, gender (AUC = 0.93), sexual orientation , political party (AUC = 0.85), and race (AUC = 0.96) were well predicted, with other outcomes , moderately predicted. Kosinski, Stillwell, and Graepel  exploredr = 0.56) than others’ ratings did (r = 0.49). Further, they found that Facebook likes correlated more highly with a variety of life outcomes  than self-ratings did.Youyou, Kosinski, and Stillwell  followedTo this point, we have focused on the influences of personality state and trait variables on cognitive test score performance, to make the point that cognitive test scores cannot be unambiguously attributed to cognitive skills. Instead, they represent a mixture of cognitive and noncognitive influences. In this section, we address the opposite question, “To what extent do cognitive factors influence responses to personality assessments?”There is an established literature on personality change, suggesting that personality generally gets better with age. That is, based on rating scale responses, from young to later adulthood (ages 21 to 60), conscientiousness and agreeableness increase over time and neuroticism declines . HoweverThe differentiation hypothesis of cognitive ability is based on the idea that the role of the general factor diminishes during development (or with increases in ability), dominating early childhood , but becoming increasingly less important with development (or at high ability levels), as the role of specialized abilities  become relatively more important. The support for this hypothesis is mixed , but a qMõttus, Allik, and Pullman  found thIn another manifestation of the importance of ability in responding to personality surveys, adults are quite capable of ‘faking’ responses to personality scales  to present a favorable impression. The degree to which they are able to do so is related to cognitive ability . The typical rating scale format of a personality test enables ‘faking’. That is, to convey a positive image of oneself, it is possible to “strongly agree” with positive statements  and “strongly disagree” with negative ones , regardless of one’s personality. In fact, there is mixed evidence on the extent to which respondents actually do this, some suggesting respondents do not often fake , others The anchoring vignettes technique is a method for increasing comparability between respondents on rating scale measures, by having respondents rate both themselves and others, described in vignettes, on the same items . AnchoriJournal of Intelligence on the integration of personality and intelligence, which invited contributions to “bring these two traditions  back to the discussion table and to underscore the relevance of an integrative perspective for both individual differences and developmental research” [This article is a contribution to a special issue of the esearch” . A centrAnother theme is that, while construct and method are typically confounded—intelligence is measured with tests and personality is measured with Likert scale self-reports—in principle, they are separable, and failing to acknowledge the construct-method distinction results in dubious conclusions, such as the highest personality correlate of intelligence is the openness/intellect factor. That conclusion could nearly as justifiably be restated as the method effect for measuring intelligence is observed directly as the correlation between the openness/intellect factor  and intelligence test scores.New York Times by a prominent U.S. Senator, “We need immigrants with skills. But working hard is a skill” [We have several suggestions for how personality and intelligence research and researchers can move forward together. First, we believe that the constructs of intelligence and personality are viable. Carroll’s  cognitiva skill” . The misSecond, as psychology and measurement psychology in particular has been recognized for a long time, it is useful to include multiple measures of a construct to unconfound the construct from its measurement. For new surveys, this might include supplementing Likert scale measures with additional behavioral, or ‘objective’ measures. And fortunately, as the literature review above shows, there may already exist multiple measures of constructs in extant survey datasets. For example, large-scale achievement surveys, such as NAEP and PISA, have been and can continue to be analyzed for indications of personality traits, such as tendency to put forth effort, by analyzing achievement item response times, item position effects, and the like. There are a large number of potential datasets that could be mined for personality indicators other than Likert scale measures.Journal of Intelligence would be useful moving forward.Finally, we suggest expanding the editors’ call to bring personality and intelligence researchers back to the discussion table to include behavioral, labor, and education economists. As we hope this review demonstrates, economists have already made significant contributions to our understanding of the integration of personality and intelligence. There are the beginnings of integration with occasional special conferences , Nationa


The identification of sections in narrative content of Electronic Health Records (EHR) has demonstrated to improve the performance of clinical extraction tasks; however, there is not yet a shared understanding of the concept and its existing methods. The objective is to report the results of a systematic review concerning approaches aimed at identifying sections in narrative content of EHR, using both automatic or semi-automatic methods.This review includes articles from the databases: SCOPUS, Web of Science and PubMed (from January 1994 to September 2018). The selection of studies was done using predefined eligibility criteria and applying the PRISMA recommendations. Search criteria were elaborated by using an iterative and collaborative keyword enrichment.Following the eligibility criteria, 39 studies were selected for analysis. The section identification approaches proposed by these studies vary greatly depending on the kind of narrative, the type of section, and the application. We observed that 57% of them proposed formal methods for identifying sections and 43% adapted a previously created method. Seventy-eight percent were intended for English texts and 41% for discharge summaries. Studies that are able to identify explicit (with headings) and implicit sections correspond to 46%. Regarding the level of granularity, 54% of the studies are able to identify sections, but not subsections. From the technical point of view, the methods can be classified into rule-based methods (59%), machine learning methods (22%) and a combination of both (19%). Hybrid methods showed better results than those relying on pure machine learning approaches, but lower than rule-based methods; however, their scope was more ambitious than the latter ones. Despite all the promising performance results, very few studies reported tests under a formal setup. Almost all the studies relied on custom dictionaries; however, they used them in conjunction with a controlled terminology, most commonly the UMLSⓇ metathesaurus.Identification of sections in EHR narratives is gaining popularity for improving clinical extraction projects. This study enabled the community working on clinical NLP to gain a formal analysis of this task, including the most successful ways to perform it. The wide adoption of health information systems has produced billions of Electronic Health Records (EHR) all over the world . StructuUnfortunately, secondary use of EHR content is not a straightforward task due to the need to deal with its hybrid structure, ranging from coded or structured data, over free-text database entries, entire documents, and let alone multimedia content. EHR systems that include solely coded data provide a great support for billing, quality assurance or decision support, but they have significant limitations, including the difficulty identifying medically relevant aspects resulting in a loss of information , which hRegarding the internal structure of health care documentation, it typically follows the guidelines for interactions between health care professionals and patients , APIE  , 23, 63)With the aim of extracting structured information from clinical narratives by automatic or semi-automatic methods and systematically enhancing them for secondary use, different strategies have been proposed as part of the initiatives of Clinical Information Extraction . These tSection Identification, is defined as detecting the boundaries of text sections and adding semantic annotations. We define a section as a text segment that groups together consecutive clauses, phrases or sentences that share the description of one dimension of a patient, patient’s interaction or clinical findings. A section can be marked explicitly, through structural demarcations (headings or subheadings), or can exist implicitly. The main assumption for making this identification is that presumably unstructured texts, actually have an explicit (i.e. marked by headings or using predefined section delimiters) or implicit structure defined by clinical authors.One option towards addressing the aforementioned problems is adding some structure to clinical narratives through the identification and interpretation of sections. This task, called from now on Chief Complaint, Physical Exam or Assessment ; ot; otChiefry (e.g. ). In theWhat are the characteristics of the studies that propose or include a strategy for section identification,What are the methods used for the identification of implicit or explicit sections,In which application scenarios section identification has been used,In which contexts section identification has demonstrated good performance,Which terminology systems have been used for characterizing sections.This review aims to contribute to a shared understanding of this concept by answering the main research questions: clinical narrative they processed, and the terminologies employed.In order to answer these questions, we carried out a systematic review of available section identification approaches, including the methods, the assumptions they made, the kind of clinical narrative as a report-style free-text used for clinical documentation. Clinical narratives can occur as clinical notes (e.g. progress notes) in free-text fields in EHR databases, but also as fully-fledged documents .In the remainder of this article, we use the term Even though section identification in clinical narratives has been described in several studies, there is no consensus about the terms used to refer to it. Therefore, during the first phase of this review, it was necessary to iteratively identify the list of relevant terms that allowed us to perform a complete literature search.The search strategy was divided into three parts, each of them associated with one aspect: what should be identified, from where it should be identified and how it should be identified. The first aspect was subdivided into two parts: what is the intended action and what is the object of that action. The resulted terms related to the action were: “labeling”, “tagging”, “detection”, “annotation”, “mapping”, “identification”, “segmentation”, “classification”, “decomposing”, “evaluating”, “assessment”, “validation”, and several variations, for instance “annotated”, “label”, “mapped”, among others. The terms employed to describe the object were: “zone”, “segment”, “section”, “header”, “paragraph”, “sector”, and their variations as “subsections”, “headings”, among others.The purpose of the second aspect was to define the scope of the analysis, because this review is intended to analyze studies exclusively in clinical narratives, and not in other types of documents, especially not scientific abstracts. This term comprises those core parts of the medical record that are relevant for the provision of care, e.g., medical history, nursing notes, reports of special procedures, and summaries. Thus, we included the terms or acronyms: “EHR”, “EMR”, “clinical record”, “medical record”, “health record”, “patient record”, “clinical text”, “medical narrative”, “clinical narrative”, “clinical report”, “clinical note”, “clinical document”, and their plural variations.automatic or semi-automatic processes, resulting in the inclusion of the following terms: “language processing”, “NLP”, “machine learning”, “mining”, “data analytics”, “corpus”, “text analysis”, “medical informatics”, “deep learning”, “big data”, “artificial intelligence”, “retrieval”, “automated”, “information extraction”, “pattern recognition”, and variations thereof.Considering the scope of this review, we also included a set of terms that allowed us to identify studies that involved The explicit inclusion of term variations in each part of the search expression was important, because the query mechanism of some literature databases is not tolerant regarding morphology (e.g. stemming or lemmatization). The integration of the final set of terms was made using the OR operator within each query aspect and the AND operator between the aspects.IC1: The study proposes, uses or evaluates a section identification task on EHR narratives. IC2: The section identification task is explained and, it is done using an automatic or semiautomatic method.Considering our research questions, the eligibility criteria comprised two inclusion criteria: IC1 allows us to include three types of studies: 1. Studies whose main goal is proposing a section identification method. 2. Studies that use a section identification method in order to achieve another objective of greater scope, such as named entity recognition or de-identification. 3. Studies that evaluate a section identification method proposed in another work or using a specific software tool.IC2 allows us to include two types of studies: 1. Studies that elucidate the method they propose. 2. Studies that apply and explain a method proposed by others.Studies from 1994 until 2018 in any language were eligible. 1994 was chosen because, after this year, the popularity of the internet promoted the creation of automated information systems, including EHR systems. These studies were identified in three well-known databases: SCOPUS, Web of Science and PubMed using the search query previously defined.Our selection process is depicted in Fig. In addition, the authors checked in the source databases PubMed and Scopus the list of similar or related articles to the relevant ones. Finally, one of the authors searched on Google Scholar for another set of possibly relevant articles. After verifying that the obtained articles were different from those previously reviewed, the final list included nine (9) additional relevant studies. Finally, 39 articles were studied in depth. Studies on sentence-level chunking were discarded (e.g. ).For the analysis, we grouped the studies with the same method, the same focus and with at least one common author, e.g., two of the articles focusing on the SecTag method were put in one group Denny et al. , 10, andDuring the analysis we found that each study addressed the section identification task with a different focus; a subset addressed it as part of a comprehensive text analysis pipeline; others addressed it as a primary task that, given its complexity, required a major effort to achieve good results and, as a consequence, some of them produced modular solutions.As Table Studies on section identification vary greatly depending on the kind of narratives and the type of sections they are focused on. This section describes the scope of each study in terms of the kind of section that it is able to recognize, the assumptions it makes during the section identification process, the type of narratives that the study addresses, and the language that it can recognize.Medical History or Medications. 46% of the studies also detect subsections, e.g., Allergies within Medical history or Specimens within the Diagnosis section of a pathology report. Some articles are only concerned in detecting one general section, like Singh et al. [Impression section from radiology reports, or Mehrabi et al. [Family history section and its subsections. Besides section types, the scope of some studies is limited by their methodological assumptions. The most common assumption is that all sections are preceded by sections headings. We refer to this as explicit sections. Forty-six percent of the studies subscribe to this assumption. Other ones detect implicit sections, i.e., sections without headings (8%) or can detect both (46%). Regarding the language of the narratives, English is leading with 78%, with the rest split between German, Korean, Finnish and French. This reveals a large gap regarding solutions tailored to other languages. The type of narrative showed a large diversity, with discharge summaries leading with 41% of the studies, probably because they usually contain an internal structure defined by local or institutional regulations or because they are frequently transferred between practitioners, or institutions. Radiology reports are in second place in 24% of the studies.As shown in Table h et al. , who deti et al.  who aim Even though the EHR System from which clinical narratives were generated plays a crucial role in their structure, several studies did not report it. However, from the studies that did report the source of the narratives, we could identify that they vary considerably. Some studies used analytical repositories: the Intermountain Healthcare’s Enterprise Data Warehouse (EDW) , the VINCurrent section identification methods can be classified into rule-based (59%), ML (22%) and hybrid 19%) methods. The former ones are based on a set of rules, created by experts or using methods that explicitly describe patterns for detecting section boundaries. These rules are validated with a sample of the available texts, and then applied to new ones. ML methods train a classification model, using a text corpora in which sections were previously identified and annotated (i.e. sections are clearly identified); the generated model is then applied to classify sections in new texts. Hybrid methods take the best of both; rules that detect well-known clues first identify easily detectable sections; this step is then refined using a ML model. Table 9% methodStudies that use rules range from simple rules that match section headings against target text to sophisticated hierarchical rules. In order to characterize these studies, we use the following dimensions  followed by a colon marks the beginning of a section.Probabilistic rules: Rules that take into account probabilistic data obtained from sample data sets, such as Bayesian word probabilities occurring in each section of a sample text.Different types of rules may often be used in the same study. For instance, a method may first find standard headings using an exact matching rule and then identify variations using regular expressions.Required previous information:This dimension explores the type of external information required as input by the method. Studies included in this review use: Flat dictionaries of headings: These are plain lists that contain possible terms used as headings for each section.Hierarchical heading dictionaries: They include a hierarchy of headings, which implies a parent-child relationship that can be exploited to infer knowledge when a heading coincides with a portion of the target text.Dictionaries of synonyms and word variants, providing modifications or abbreviations of standard headings. Some of the studies include a dictionary with the most common variations or synonyms for a heading or words contained therein.Features Generated from Available Sample Texts: Some studies rely on information extracted from an available sample of texts that can give hints on the existing patterns for different sections. This dimension describes what kind of features are extracted from these texts to be used later as an input for the rules. Common features can be classified in the following groups: Formatting features: Represent the shape of a section including the characteristics of its headings , explicit line ending characters (e.g. question marks), the beginning of the lines matching an enumeration pattern, or the typical size of the section.Concepts/ terms within each section: Frequent concepts or terms occurring in each section are identified.Heading probabilities: Probabilities are assigned to common and uncommon section headings in a corpus; e.g., the probability of each possible section heading occurring in a document within a corpus.These features can only be obtained automatically if the sample data set was already annotated with section begin and end tags. Note that feature values may vary according to the corpus, which means that the rules generated using them should be dynamically adapted to new feature values to be used in different contexts .exact matching of well-known headings (and some heading variations) within clinical narratives; different studies follow this approach. Wang et al. [Impression section, which was detected using hand-crafted rules.The following paragraphs detail the characteristics of section identification studies that use a rule-based approach. Basic approaches rely on the g et al.  detect sg et al. , demonstg et al.  improvesg et al.  classifiFinally, the study of Phuong and Chau  recognizThe studies above demonstrate that exact matching of heading texts for section identification is very useful to improve the precision of this task. However, if the level of standardization of the headings is low, recall drops due to sections that cannot be detected by this method.regular expressions, and in general pattern matching, are the most common methods used for section identification. Radiology reports were analyzed using regular expressions in different studies. Taira et al. [Clinical History, Findings) and common formatting layouts . Hsu et al. [As an evolution of the rules that find the exact words of headings in the text, a et al.  and Rubia et al.  identifiu et al.  proposedPathology reports sections have also been identified using regular expressions. Schadow and McDonald  created In order to analyze the use of standard terminology in the sections of operative notes, Melton et al.  extracteSections of clinic visit notes have been identified using regular expressions. Schuemie et al.  proposedFinally, Meystre and Haug  used regIn summary, studies have used regular expressions to find headings that do not match with the standard or expected text, but with some variation of it. Besides, this type of rule is often used to recognize when a text corresponds to a heading, or conversely when it does not, analyzing the formatting characteristics .probabilistic information obtained from a corpus of clinical narratives.Complementing the exact matching and the regular expressions for section identification, a significant subset of studies include more sophisticated rules intended to detect not only labeled, but unlabeled sections. These studies typically use Denny et al.  created The study of Shivade et al.  includesThe study proposed by Tran et al.  detectedOther types of works explore section identification from a temporal point of view. Lee and Choi  developeAs can be seen, studies have used probabilistic rules mainly to deal with implicit sections, or as a mechanism to improve precision and recall of sections identified with other types of rules. Even though these rules may vary in complexity, all of them represent the knowledge acquired from a sample of texts where sections have been previously identified. The availability of this sample is a crucial constraint for using this type of rules.Finally, some studies that follow a hybrid approach apply rules during their first phases. That is the case of Ni et al. , who defThe ML method(s) used. Note that this review only illustrates the method (aka technique), since most of the studies do not specify the particular implementation or algorithm used.Strategy used for training and test data set creation. “Manually created” data sets means that human experts annotate (or label) the sections or the specific characteristics from the text required for section identification. “Using a rule-based approach or an automated method” means that the study included an automatic phase that obtained the sections using rules. Finally, “Active learning and distant supervision” means the study facilitates the creation of training and test data sets by combining automatic with a manual method in order to reduce the human effort.The reported size of the training data set. Although some studies reported their training sets in terms of sentences, sections, or documents, this review unifies the metric as the number of clinical narratives used.The reported size of the test data set. It is described using the same unit (i.e. number of documents). “CV” means Cross Validation, i.e., an approach that uses the same data set for training and testing, changing iteratively the part of the data used for either purpose.ML algorithms are increasingly relevant for section identification. Table Our analysis shows that the most popular ML methods are Conditional Random Fields (CRF) and Support Vector Machine (SVM). The Viterbi algorithm is also very popular to find the optimal section label sequence where section identification is treated as a sequence labeling problem. All these works rely on manually created training and test sets, at least partially.The size of the training data set differs considerably between studies, ranging from 50 to 25,842 texts. Size variation notably impacted the performance and adaptability of the models on new data sets.Lexical features: word-level features that can be extracted directly from the text. E.g., the entire word, its prefix, its suffix, capitalization , its type , among others.Syntactical features: represent the sentence structure, e.g. grammatical roles and part of speech.Semantic features: represent the meaning of words and terms in a sentence. Most of them refer to a custom dictionary or a controlled terminology.Contextual features: describe relative or absolute characteristics of a line or a section within the clinical narrative. For instance, the position of the section in the document and layout characteristics.All studies extracted features relevant for section identification. Table before, after and incomparable.All studies that applied ML are methodologically similar, but each of them has certain restrictions according to the type of sections it seeks to identify. For instance, the approach proposed by Bramsen et al.  exploresLi et al.  considerThe study of Waranusast et al.  contribuTepper et al.  used a mThe approach for section identification proposed by Mowery et al.  classifiLohr et al.  developeIn Deléger and Névéol , sectionHaug et al.  used BayEmergency Reports, could be applied on reports with similar structure and lexical distribution, such as History and Physical Exams, Progress Notes and Discharge Summaries. Likewise, Bramsen et al. [Discharge Reports, can also be used during the pre-processing phase on other analysis.Even though the above studies have trained their models on data sets from specific institutions, their underlying ML logic gives them the possibility to retrain the original model on new training data, in such a way that the section identification method can be applied in other institutions. Tepper et al. , for insn et al.  claimed Finally, seven hybrid approaches use rule-based methods during the creation of training and test data sets, and then apply ML methods. This is the case of Apostolova et al. , SadoughThe study by Cho et al.  extractsFindings and Impression. Other sections, e.g., Recommendation proved to be hard to classify correctly.In a similar vein, Apostolova et al.  used traSadoughi et al.  embeddedSpeech recognition was also the context of Jancsary et al.  approachChen et al.  and Dai Ganesan and Subotin  proposedNi et al.  used actIn many NLP projects, section identification is just one task that is expected to enhance the accuracy of other tasks like information extraction. Such application scenarios are elucidated in Table focus the search on a specific section or the opposite, to augment the number of sections where the search should be applied.Section identification enhances unstructured text by a rough. That is why some studies in EHR have used the detected sections to build structured data repositories or robust structured resources that can be easily queried and analyzed by an institution or shared among researchers , 39, 45.Impression section from radiology reports [viz. the sections postoperative diagnosis, chief complaint, diagnosis in discharge summaries. Wang et al. [Doan et al.  used sec reports . Besides reports  improved reports  improved reports  improved reports  used secg et al.  used cong et al.  extracteg et al.  used secg et al. , co-refeg et al.  proved tg et al.  and Lee g et al.  followedFinally, section identification had been used for education and quality assurance. Denny et al.  e.g., usThis section focuses on the performance metrics, reported by proponents of section identification approaches. Such metrics varied greatly according to the test data sets used during their experiments. Regarding the metrics, some of them discriminated performance for labeled and unlabeled sections, while others reported one measure, regardless of section type. Likewise, some studies aimed at accuracy only, while others specified precision, recall, and f-measure. Observing the number of measures reported, some studies included a huge number of experiments, and sometimes, presented their results not as an absolute value, but as a range of values (e.g. precision range). Other studies did not report any performance measure, especially when rule-based methods alone were employed. From an experimental point of view, the majority tested their methods using data sets obtained from the same data source from which the training data was extracted. This implies that most of them have not been tested to be useful in different institutions, except one hybrid method aiming at evaluating its adaptability by using data from several institutions .Considering these performance result differences and being aware of the difficulties in comparing experimental results, we examined the performance reported for each study and extracted precision, recall, F-measure or accuracy, when available. For studies reporting more than one experiment, we took their best result. For those that reported their performance results using a numerical range or reported individual results for each class value, we calculated the average.%, Precision median =90.1%), whereas the recall was in general lower (i.e. median =84.55%). Contrary to the intuition, the recall from rule based methods was better than the recall using ML and hybrid methods.Figure Family history section . Such a specific result cannot be compared to the experiments able to extract any section. Nonetheless, the rule-based method proposed by Denny et al. [%, recall =99%) in the analysis of “history and physical” notes.Only three out of 23 studies reporting on a rule-based method published results , 27, 58,%), by Waranusast et al. [%) and by Mowery et al. [%), the first one using a hidden Markov model and the others a SVM model. However, their experimental setups are not comparable; e.g., the experiments presented in Mowery et al. [From the group of studies that used ML methods (i.e. eight studies), the highest performance was achieved by Li et al.  , who used an expectation maximization classifier and a CRF model, respectively. However, the experiments reported by Cho et al. [%) with different types of texts from different institutions. They reported experiments with 12502 records using a hold out validation approach. This is the only study that sought flexibility in its proposal.In general, the recall in the ML group is the lowest. It can be argued that this is the rationale for combining ML models with rules. In fact, the hybrid methods under scrutiny achieved better performance results than methods based on ML alone. The best scoring ones are the methods proposed by Cho et al.  , ranging from term lists, hierarchies to ontologies. The most common controlled terminology used is UMLSⓇ 8 studies in eightIn section identification UMLSⓇ has been used to parse and transform narratives into structured representations consisting of codes , 49, 60.HL7-CDA is a standard that describes the structure and semantics of a clinical document for the purpose of exchange . The ideThe “Consumer Health Vocabulary” is also included in the UMLSⓇ, and its purpose is to connect informal, common words and phrases about health to technical terms used by health care professionals . ShivadeFinally, Wordnet, a lexicon mainly for the English language, was used in the method proposed by Jancsary et al.  for deteThis review of the state of the art in section identification in clinical narratives is the first in-depth review of the methods for structuring clinical narratives through section identification and annotation, comprising 39 studies (37 unique studies).Treatment and Recommendations are sometimes merged, as well as Anamnesis and Medical and risk factors, and sections are omitted when considered irrelevant, e.g., Medical comments. Besides, they also found that some sections were domain dependent, e.g., Family medical history had only been included when relatives had been diagnosed with a relevant disease.No more than half of the studies were based on formal methods, whereas the other ones used or adapted previously created or proposed approaches that addressed their specific needs, but were not tested in other contexts. Less than one fifth operated languages other than English. The assumption that sections have headings was made in nearly half of the works. This assumption is problematic because sections without headings are very common (two thirds according to ). AlthouIn the studies under examination, very little information was available regarding text production details or the exact source of the texts. It is sensible to assume that the more freedom a clinical author has, the less standardized section headings can be expected and the more often sections without heading will be found. In contrast, where clinical texts obey immutable, pre-defined document templates, no NLP methods for section identification are needed.There are different granularity levels under scrutiny. Whereas some studies considered classical dissections of clinical documents according to templates like SOAP, others dissected texts according to the temporal changes referred to in the text, and others just distinguished clinically relevant content from institutional/administrative information such as found in headers and footers.Regarding the technologies employed, three groups are distinguished: rule-based, ML and hybrid. Most rule-based approaches require sections with headings, which are processed using string and pattern matching. Others, using probabilistic rules based on text features, produced very good results. ML methods treat section identification as a classification problem, with CRF and SVM being the most popular paradigms. A bottleneck is the time-consuming production of training data, with the drawback that the models are difficult to adapt to other contexts. Typical training features were lexical, syntactical, semantic or contextual ones. Finally, hybrid approaches used rule-based methods, often during the creation of training data sets, and then applied ML methods.The predictive value of the features used by some of the ML and hybrid studies revealed remarkable phenomena. E.g., features like token unigrams and n-grams, word/POS pairs, features indicating whether the text contains a known section heading, and layout features had a substantial positive impact on the classification. In addition, the use of non-standard abbreviations in section headings and the appearance of phrases in sections that do not correspond constituted major reasons of incorrect classifications. Similarly, a decrease in precision on the section identification was observed when a very specific terminology for sub-headings was used.Almost all the studies relied on a custom dictionary; mostly constructed by the authors themselves, occasionally re-using content from existing controlled terminologies. Here, SecTag, an extract of several terminologies is gaining popularity . The mosThe contexts where section identification has been used was analyzed considering the specific application and the involved type of narrative. Application contexts are diverse; ranging from the generation of structured data for analytical purposes to the transfer of documents to external parties. Information retrieval of EHR plays a significant role like the support of cohort selection or the identification of patients with risk factors. There is also experimental evidence that co-reference resolution and named entity recognition are improved when using the section as another feature of the model. Sections have also been useful for disambiguating whether a text token is a sensitive entity in EHR de-identification tasks. Likewise, the identification of temporal sections on clinical narratives has been applied to improve tasks that bring events in a specific order. Section identification was also used to support the quality assessment of clinical texts, e.g., for measuring their level of compliance with institutional guidelines. Similarly, section identification has been used to extract relevant concepts from texts produced by students in order to select supportive educational resources for the specific topic students were coping with.Regarding the text types under scrutiny, discharge reports constitute the leading text genre, followed by radiology reports, visit notes, and pathology reports in decreasing order. The large number of possible notes reveals the lack of a standardized typology of clinical narratives.Performance analysis showed that most reported results are incomparable due to the absence of any reference standard for section identification evaluation. Although often viewed as a classification task, the level of granularity of the classification largely varies from studies that focused on the token or sentence level, to studies that focused on section level.Aware of these limitations, we examined the reported measures in each study and found that precision and recall are high. However, recall is always lower in the reported studies. We also found that the more restricted the scope in terms of text genre, medical specialty or institution the better the results. Regarding the kind of method, we found that most of the studies with rule-based methods did not report any kind of evaluation measure, but the ones that reported obtained very good results. Hybrid methods got better results than ML methods, but lower than rule-based methods. However, their scope was more ambitious than rule-based studies in terms of the type of notes they can deal with. The lack of large data sets for training and for testing is one of the major difficulties in all the studies that used a ML method.There are several limitations to this review. First, it may have missed relevant articles that were not indexed in the selected databases. Second, though the definition of the keywords involved much work and several iterations, we could have missed some studies that used less common language terms to refer to section identification or which were written in other languages. Finally, given the nature of this review, viz. an overview of academic literature, we are aware that much work done in the development of industrial products is not reflected. Neither did we analyzed patents related to section identification approaches that are not paralleled by academic papers.We have reviewed and characterized current methods for identifying sections in clinical narratives from EHRs, by analyzing 39 studies. There is clear evidence that they improved the understanding of clinical narratives, which has a positive impact on the performance of different NLP tasks. Thus we recommend that section identification should be included in NLP pipelines that handle unstructured clinical narratives.For clinical NLP in general and for section detection in particular, a major challenge is the lack of pre-existing annotated corpora. One reason for this is data privacy. The effort needed for clinical document de-identification and the often unclear legal status of de-identified clinical documents for research explains this situation. Besides, it is unclear to which extent a certain de-identification protocol can prevent re-identification, especially for some type of documents (e.g. discharge summaries).To deal with this problem, a classic solution would be to use rules that embody the characteristics of the sections. However, this solution reduces the generalizability to other narratives from the same type, but from different authors and institutions. Combining supervised with unsupervised ML methods that analyzed semantic similarities of known and unknown EHR structures may be a good path to create more stable solutions; unfortunately, this path has not yet explored in depth for section identification.Reported evaluation experiments of current section identification methods may be improved with an evaluation framework that defines measures enabling result comparison, independently of the proposed method, and that includes an open benchmark data set. The challenge for the community is then to produce this benchmark data set, integrating narratives from different institutions, and agree on the type of measures to be evaluated. This should be done for different natural languages.Another unexplored issue is the automatic recognition of the conceptual model behind the clinical narratives; there are significant differences in the assumptions practitioners make when dealing with clinical data on a timeline in chronological order than when dealing with problem-oriented EHR, or source-oriented EHR, where texts are organized according to the method by which the information was obtained. Dealing with these differences in the written approach is a challenge that has to be fulfilled to create more generalized solutions. The interdependencies between EHR paradigms and workflows on the one hand and clinical text, on the other hand, requires further investigation.The analysis of the applications allowed us to detect that when a method of section identification is used within a broader task, generally the simplest methods with low recall are used. The challenge, therefore, is to use the most robust methods and integrate them into high-impact clinical NLP tasks such as clinical entity recognition, acronym or abbreviation expansion, and patient record summarization.Finally, we would like to state that in this review, we have focused on the analysis of methods published in research papers available in the selected scientific databases. It is necessary to explore other solutions available in industry-software or patents, which may lead to other findings. We also anticipate that the current and future work on section identification is being strongly influenced by the current paradigm shift towards neural network-based high-performance computing, which may affect the relevance of some of the approaches analyzed in this review.