This review analyzes the different ways of applying the subscapular system of flaps (SSSF) as a convenient and versatile solution for a large variety of head and neck oncologic defects.The ventral approach permits safe and efficient harvest of various chimeric SSSF in a supine position, thus allowing simultaneous flap preparation and tumor ablation. Conformational studies have revealed how similar the tip of the scapula is to the hard palate in terms of dimensions, shape, and conformation. This has led to favor horizontal placement of the scapular tip for palate reconstruction in most instances, addressing the vertical extension of the postmaxillectomy defect using denuded bony grafts surrounded by well vascularized chimeric muscular components.The SSSF possesses an unparalleled versatility to efficiently address small-medium sized soft tissue defects up to vast and complex composite resections. The chimeric components of these flaps benefit from a considerable independency provided by the length of the named arteries arising from the thoracodorsal pedicle, offering a high degree of freedom to accomplish the required in-setting. This reconstructive option should be implemented in every head and neck surgical team and offered to suitable patients. Surgical resection of head and neck tumors may produce a wide variety of soft and bone tissues losses and, therefore, in order to accomplish effective restoration of their original form and function, every head and neck defect should be precisely anticipated and evaluated in terms of lack of support, cover, and lining. In this light, the subscapular system of flaps (SSSF) has been recognized as one of the most versatile for head and neck oncologic reconstructions –2, sinceA comprehensive detailed description of the subscapular vascular anatomy and its possible variants is beyond the scope of this work and was summarized in a recent review . The subThe TDA usually divides into three branches: the first in descending order is represented by a branch for the latissimus dorsi and overlying skin. As recently described , brancheThis review analyzes how the SSSF plays an important role in a variety of head and neck oncologic reconstructions, from simple medium-sized soft tissue replacements to the restoration of complex three-dimensional, composite defects. Our strategies of using various combinations of chimeric harvests for bony and composite reconstructions will be also outlined. et al.[▪▪], it is our experience that every type of SSSF harvest, from the septocutaneous TDAP to the more complicated composite chimeric flaps, can be easily and safely raised in a supine position during simultaneous tumor resection.One of the most discouraging aspects concerning the SSSF has been traditionally the claim that when using such free flaps the patient should be turned in a lateral decubitus, or even in a prone position, and then turned back, with obvious increase of time spent, surgical complexity, and overall workload for the operatory room team. Patient positioning at 30° lateral decubitus was described to overcome the need of turning the patient, allowing a two-team approach ; howeveret al.▪▪, it isUsing such an approach, the patient is placed with the thorax projecting a few centimeters over the lateral edge of the surgical bed at the selected side, while a latex wedge is located under the ipsilateral iliac crest. With one hand the assistant holds the patient's forearm, while with the other the shoulder is sustained during the entire harvest. The arm is abducted less than 90° with the forearm flexed at 90°. The reconstructive assistant is standing next and immediately behind the first assistant of the ablative team. During tumor resection , the flap is simultaneously harvested from the contralateral side. The skin incision starts from the axillary midpoint, following down the projection of the anterior edge of the latissimus dorsi muscle, and is adjusted for the intended skin paddle. The anterior edge of the latissimus dorsi is first identified, while ventral dissection of the muscle reveals its muscular hilum and the entire circumflex and thoracodorsal vascular anatomy, allowing the harvesting of the entire gamma of SSSF needed in this area. With such a ventral approach, after identification of the angular branch of the TDA, if a tip scapula free flap is planned, it is very convenient to pinch the tip of the bone with a solid Backhaus clamp, since its anterior and lateral traction greatly facilitates dissection of the flap and enhances visualization before osteotomies.Soft tissue defects within the neck, oral cavity, oropharynx, hypopharynx and larynx may require transposition of pliable tissues, restoring the separation between the aerodigestive tract and neck compartments, with the aim of enhancing the residual functions of preserved structures around the resection, especially when the tongue is involved. For these purposes, fascio-cutaneous flaps such as the radial forearm or the anterolateral thigh are extensively used. However, the SSSF can offer competing alternatives, represented by the parascapular fascio-cutaneous, the TDAP Fig. a and, foIn case of total glossectomy defects, especially if the larynx has been preserved, the myo-cutaneous latissimus dorsi free flap offers an adequate amount of bulk to create a new tongue with vertical projection thus providing functional tongue-palate competence and allowing to squeeze the bolus from the oral cavity toward the oropharynx Fig. b. This fIn case of pharyngo-laryngectomy or total glosso-laryngectomy defects, the latissimus dorsi myo-cutaneous flap represents a reliable reconstructive option that can be transposed as either a free or pedicled flap. However, these techniques should be considered as second-line options, especially for muscular and thick males, in which the myo-cutaneous paddle of latissimus dorsi may represent an obstacle in distal suturing to the cervico-mediastinal stump of the esophagus, thus increasing the risk for pharyngo-cutaneous fistula and stricture. Therefore, in these clinical scenarios, thinner fascio-cutaneous free flaps such as radial forearm and anterolateral thigh still represent the workhorse donor sites –13.On the contrary, the transposition of a pedicled latissimus dorsi flap is a reliable solution for a wide spectrum of large head and neck mucosal and skin defects Fig. c. Its wiTo fill the gap after extended parotid resections, the TDAP flap can be de-epithelized to replace the tissue loss resulting from extirpation of the parotid gland and, when the overlying skin is involved by tumor, this flap may be transposed as a fascio-cutaneous paddle to restore the cutaneous defect . The SSSFor neck, facial, and scalp reconstructions, whenever a large amount of pliable tissue is needed, and dural exposure or transgression with ensuing reconstruction has been accomplished, the SSSF provides excellent solutions Fig. d-f. It u▪▪,In the last decade, the SSSF has gained increasing popularity until becoming the preferred method for reconstruction of postmaxillectomy defects ,5,9▪▪,169The bony component of the scapular tip flap provides a triangular flat bone, whereas maxillectomy defects greatly differ in terms of tridimensional bony loss, and thus the reconstructive procedure must overcome and minimize this discrepancy by adequate customization of the reconstruction itself. Taking advantage from the evidence that conformational studies have revealed how similar the tip of the scapula is to the hard palate in terms of dimensions, shape, and conformation , in our Proportionally to the vertical extension of the defect, bony grafts from redundant portions of the scapular flap, obtained by setting the osteotomy more cranially than required, are used to replace the anterior maxillary wall (Brown class 2) or to reconstruct the orbital floor . These grafts must be completely denuded from their original muscular attachments, and must be surrounded by well vascularized tissue , the in-setting of the scapula tip depends upon the amount of hard palate resection. If the contralateral alveolar ridge is preserved , the scapular tip is placed vertically, with the angle replacing the most anterior and medial portion of the resected superior alveolar ridge. The medial scapular border is fixed against the nasal osteotomy, the lateral scapular border replaces the resected alveolar ridge and is fixed to the malar bone, and the osteotomy edge replaces the resected orbital rim and is shaped/drilled to match the contralateral healthy side. The chimeric latissimus dorsi muscular portion is used to obliterate the orbital-maxillary cavity, and to reconstruct the remaining posterior portion of the hard palate  Fig. 2gg2g,h.If the hard palate resection includes more than 50% of the alveolar ridge , the scapular tip is fixed horizontally thus fully replacing the hard palate. The orbital-maxillary cavity is obliterated with the latissimus dorsi and a scapular bony graft wrapped by latissimus muscle is used to reconstruct the inferior orbital rim and anterior maxillary wall Fig. i,j. WhenDental rehabilitation of the scapular tip bone with osteo-integrated implants is controversial. Many reports claim the feasibility and reliability of placing dental implants in the scapular tip flap ,24. HoweFor resections of the orbito-zygomatic complex, restoration of the anatomical contour and position of the malar eminence and orbital rim are critical . In thisThe free fibula flap currently represents the first-choice reconstructive option in most instances of segmental mandibular resections. However, the SSSF, and especially the angular branch-based tip of scapula, offers a reliable alternative for patients who are not suitable for fibula harvesting due to previous limb trauma or severe vascular disease. Of note, the vascular pedicles nourishing the SSSF seems to be the least affected by atherosclerosis in the human body . MoreoveThe mandible is a curved bone, and the lateral border of the tip is usually employed for linear mandible defects with complex soft-tissue requirements ; howeverThe triangular shape of the scapular tip flap can be effectively used to reconstruct segmental resections of the mandibular angle, having the angle of the scapula to replace the angle of the mandible. A recent case series described the application of scapular tip free flap following mandibulectomy that included the condyle: the mandibular segment and scapular flap were cut using cutting guides and the scapular tip was placed into the glenoid fossa serving as new condyle, with good functional and aesthetic long-term outcomes .The SSSF represents a reliable option for a vast variety of head and neck oncologic defects, with no other donor site offering such a favorable toolbox of chimeric options. The relatively recent inclusion of the angular branch-based scapular tip flap as a source of vascularized bone has considerably widened the range of applications of this option for reconstructive purposes, thanks to the increased pedicle length with respect to the classic lateral border of the upper scapula based on the circumflex artery and subscapular vascular supply. The chimeric components of the SSSF benefit from considerable independency provided by the length of the named arteries arising from the main thoracodorsal pedicle. This offers a high degree of freedom to accomplish the desired flap in-setting according to a variety of possible orientation according to the specific needs of the case. The use of small denuded bony grafts nourished by surrounding vascularized muscles is a safe solution that well withstands postoperative adjuvant treatments, enhances facial contouring in maxillary reconstruction, and may also be efficient for orbital sustain to prevent postoperative diplopia. The possibility for simultaneous harvest during tumor ablation and a favorable postoperative donor site morbidity complete the convenience of this reconstructive solution, rendering the SSSF an essential tool for every head and neck surgical team dealing with complex oncologic defects.None.None.There are no conflicts of interest.


Design Thinking provides an empathy-based creative problem-solving approach to intervention and/or patient-centered care design that seeks to articulate the right problem space and propose appropriate solutions to challenges encountered by individuals in specific contexts, such as healthcare. This abstract illustrates the application of the Design Thinking approach in the context of dementia caregiving. Emory’s Roybal Center for Caregiving Mastery conducted a three-part design studio workshop with African American dementia family caregivers to understand and identify prioritized solutions to their caregiving challenges. The workshops followed the Double-Diamond design process model that is divided into four phases  and that emphasizes divergent and convergent modes of thinking. The process first encourages divergent thinking to explore a problem space and then employs convergent thinking to identify the salient problem and develop the right solution. The workshop’s Discover stage provided a broad and deep contextual understanding of the range of unmet needs African American caregivers experience in their interactions with healthcare personnel and systems. The Define phase enabled the convergent review and prioritization of the list of needs to be addressed. The Develop phase generated 17 intervention ideas. Finally, the Deliver phase focused on converging and validating the proposed interventions. The workshops concluded that having access to caregiver-focused resources supporting mental/physical self-care, such as support groups and peer programs, can be beneficial interventions to potentially improve knowledge, awareness, and caregiving mastery among African Americans American caregivers.


Background: We evaluated the implications of different approaches to characterize the uncertainty of calibrated parameters of microsimulation decision models (DMs) and quantified the value of such uncertainty in decision making.Methods: We calibrated the natural history model of CRC to simulated epidemiological data with different degrees of uncertainty and obtained the joint posterior distribution of the parameters using a Bayesian approach. We conducted a probabilistic sensitivity analysis (PSA) on all the model parameters with different characterizations of the uncertainty of the calibrated parameters. We estimated the value of uncertainty of the various characterizations with a value of information analysis. We conducted all analyses using high-performance computing resources running the Extreme-scale Model Exploration with Swift (EMEWS) framework.Results: The posterior distribution had a high correlation among some parameters. The parameters of the Weibull hazard function for the age of onset of adenomas had the highest posterior correlation of −0.958. When comparing full posterior distributions and the maximum-a-posteriori estimate of the calibrated parameters, there is little difference in the spread of the distribution of the CEA outcomes with a similar expected value of perfect information (EVPI) of $653 and $685, respectively, at a willingness-to-pay (WTP) threshold of $66,000 per quality-adjusted life year (QALY). Ignoring correlation on the calibrated parameters’ posterior distribution produced the broadest distribution of CEA outcomes and the highest EVPI of $809 at the same WTP threshold.Conclusion: Different characterizations of the uncertainty of calibrated parameters affect the expected value of eliminating parametric uncertainty on the CEA. Ignoring inherent correlation among calibrated parameters on a PSA overestimates the value of uncertainty. Decision models (DMs) are commonly used in cost-effectiveness analysis where uncertainty in the parameters is inherent . The impThe parameters of DMs can be split into two categories, those obtained from the literature or estimated from available data  and those that need to be estimated through calibration . External parameters are estimated either from individual-level or aggregated data that directly inform the parameters of interest. There are recommendations on the type of distributions that characterize their uncertainty based on the characteristics of the parameters or the statistical model used to estimate them . For exaModel calibration is the process of estimating unobserved or unobservable parameters by matching model outputs to observed clinical or epidemiological data  . While tA sample of calibrated parameter sets that correctly characterizes the uncertainty of the calibration target data is obtained from their joint distribution, conditional on the calibrated targets. To obtain the joint distribution, calibration could be specified as a statistical estimation problem under at least two different frameworks, through maximum likelihood (ML) or Bayesian methods. ML can fail in obtaining interval estimates by not being able to estimate the Hessian matrix when the likelihood is intractable or computationally intensive to simulate and when the calibration problem is non-identifiable ; thus, wDespite their suitability to correctly characterize the uncertainty of calibrated model parameters, Bayesian methods are generally computationally expensive because they require evaluating the model thousands and sometimes millions of times. The computational burden of Bayesian methods does not seem to be an impediment when calibrating non-computationally intensive DMs  . Still, https://science.osti.gov/ascr/Facilities/Accessing-ASCR-Facilities/ALCC). However, implementing dynamic calibration algorithms for HPC resources has generally proved difficult, requiring specialized knowledge across various disciplines. The Extreme-scale Model Exploration with Swift (EMEWS) framework was designed to facilitate large-scale model calibration and exploration on HPC resources (https://emews.github.io), which includes links to tutorials.However, the increasing availability of high-performance computing (HPC) systems in an academic, national laboratory and commercial settings enables such systems for model calibration and model exploration of microsimulation DMs at a large scale to a broader audience. HPC resources allow running large numbers of DMs concurrently, allowing calibration algorithms to generate large batches of parameters simultaneously, such as the incremental mixture importance sampling (IMIS) described below, to be run efficiently. In many cases, particularly in the academic and national laboratory settings, computing allocations can be obtained through proposals with no cost to researchers  Leadership Computing Challenge (ALCC), esources  to a broesources , to contThe purpose of our study is threefold. First, to use recently developed HPC capabilities to characterize the uncertainty of calibrated parameters of a microsimulation model of the natural history of colorectal cancer (CRC). Second, to explore the impact of different approaches to characterize the uncertainty of calibrated parameters on decision uncertainty, and third, to use VOI analysis to quantify the value of eliminating parameter uncertainty when assessing the cost-effectiveness of CRC screening.We developed a microsimulation model of the natural history of CRC and calibrated it using a Bayesian approach. We then overlaid a simple CRC screening strategy onto the natural history model and conducted a cost-effectiveness analysis (CEA) of screening, including a PSA. Instead of using the posterior means to represent the best estimates of each calibrated parameter, we obtained the posterior distribution using a Bayesian approach that represents the joint uncertainty of all the calibrated parameters that can then be used in a PSA. We then evaluated the impact of different approaches to characterize the uncertainty of calibrated parameters on the joint distribution of incremental costs and incremental effects of the screening strategy compared with no screening through a PSA while also accounting for the uncertainty of the external parameters . Finally, we quantified the amount of money that a decision maker should be willing to spend to eliminate all parameter uncertainty ).We developed a state-transition microsimulation model of the natural history of CRC implemented in R  based onThe continuous-time age-dependent Markov process of this natural history model of CRC can be represented by an age-dependent quations P=EWe simulated a hypothetical cohort of 50-year-old women in the United States over a lifetime. The cohort starts the simulation with a prevalence of adenoma of of 0.12%  and 0.08of 0.12% , respecte tables .We used the microsimulation model of the natural history of CRC to generate synthetic calibration targets by selecting a set of parameter values based on plausible estimates from the literature  Wu et a. We simuTo state the calibration of the microsimulation model as an estimation problem , we defiTo calibrate on σta . That isrameters . We defihttps://rcc.uchicago.edu/resources/high-performance-computing). Midway2 is a hybrid cluster, including both central processing unit (CPU) and graphics processing unit (GPU) resources. For this work, we used the CPU resources. Midway2 consists of 370 nodes of Intel E5-2680v4 processors, each with 28 cores and 64 GB of RAM. Using EMEWS, we developed a workflow that parallelized the likelihood evaluations over 1,008 processes using 36 compute nodes. In other words, we reduced the computation time approximately by 250 had the analysis been conducted in a laptop with four processing cores.To conduct the Bayesian calibration, we used the incremental mixture importance sampling (IMIS) algorithm , which hConsistent with previous analyses, we deemed that convergence had occurred when the target effective sample size (ESS) got as close as 5,000 . An advaWe sampled 5,000 parameter sets from the IMIS joint posterior distribution for the nine calibrated model parameters. To compare the outputs of the calibrated model against the calibration targets, we propagated the uncertainty of the calibrated parameters through the microsimulation model of the natural history of CRC. We simulated a cohort of 100,000 . We generated the model-predicted adenoma and cancer outcomes for each of the 5,000 calibrated parameter sets drawn from their joint posterior distribution. We computed the 95% posterior predicted interval (PI), defined as the estimated range between the 2.5th and 97.5th percentiles of the model-predicted posterior outputs to quantify the uncertainty limit model outputs.With the calibrated microsimulation model of the natural history of CRC, we assessed the cost-effectiveness of 10-yearly colonoscopy screening starting at age 50 years compared to no screening. For adenomas detected with colonoscopy, a polypectomy was performed during the procedure. Individuals diagnosed with a small or large adenoma underwent surveillance with colonoscopy every 5 or 3 years, respectively. We assumed screening or surveillance continued until 85 years of age. Individuals with a history of polyp diagnosis had higher recurrence rates after polypectomy, that is, a higher transition rate from normal to small adenoma  estimate, defined as the parameter with the highest posterior density. The third approach considers uncertainty only in the calibrated parameters characterized by their joint posterior distribution and no uncertainty in the external parameters, fixed at their mean values. The fourth approach considers uncertainty in both types of parameters, but instead of using the IMIS posterior distribution of the calibrated parameters, we constructed distributions based solely on the IMIS posterior moments  and the type of calibrated parameters ignoring correlations.We performed four different approaches to quantify the uncertainty of the two types of parameters—calibrated parameters and external  parameters. The first approach for uncertainty quantification considers uncertainty in both types of parameters, with uncertainty of the calibrated parameters characterized by their joint posterior distribution obtained from the IMIS algorithm. The second approach only considers uncertainty in the external parameters while fixing the calibrated parameters at the We conducted a PSA to evaluate the impact of uncertainty in model parameters on the cost-effectiveness of 10-years colonoscopy screening vs. no screening for CRC. A separate PSA was performed for the four different approaches to quantify the uncertainty of the two types of parameters. We used EMEWS to distribute the samples of each PSA across HPC resources.We quantified the theoretical value of eliminating uncertainty in the external and calibrated model parameters using VOI analysis. VOI measures the losses  from choosing a strategy given imperfect information , providiWe sampled 5,000 parameter sets from the posterior distribution using IMIS, including 3,241 unique parameter sets with an expected sample size (ESS) of 2,098. With the sample from the posterior distribution, we estimated posterior means and standard deviations, MAP estimates, and 95% credible intervals (CrI) for all calibrated parameters . The posThe Bayesian calibration also correlated the parameters, showing the dependency among some of them . There a targets .The calibrated model accurately predicted the calibration targets for both the means and the uncertainty intervals. The joint distribution of the incremental quality-adjusted life years (QALYs) and incremental costs of the 10 years colonoscopy screening strategy vs. the no-screening strategy resulting from the PSA for the four uncertainty quantification approaches of the calibrated parameters are shown in For the VOI analysis, we found value in eliminating uncertainty by having a positive EVPI in the parameters of the CEA of the 10-years colonoscopy screening strategy . HoweverIn this study, we characterized the uncertainty of a realistic microsimulation model of the natural history of CRC by calibrating its parameters to different targets with varying degrees of uncertainty using a Bayesian approach on an HPC environment using EMEWS. We also quantified the value of the uncertainty of the calibrated parameters on the cost-effectiveness of a 10-year colonoscopy screening strategy with a VOI analysis. EMEWS has been previously used to calibrate other microsimulation DMs  but has We found that different characterizations of the uncertainty of calibrated parameters affect the expected value of reducing uncertainty on the CEA. Ignoring inherent correlation among calibrated parameters on a PSA overestimates the value of uncertainty. When the full posterior distribution of the calibrated parameters is not readily available, the MAP could be considered the best parameter set. In our example, not considering the uncertainty of calibrated parameters on the PSA did not seem to have a meaningful impact on the uncertainty of the CEA outcomes and the EVPI of the screening strategy. The uncertainty associated with the natural history was less valuable than the uncertainty of the external parameters. However, these results should be taken with caution because this analysis is conducted on a fictitious model with simulated calibrated targets. Modelers should analyze the impact of a well-conducted characterization of the uncertainty of calibrated parameters on CEA outcomes and VOI measures on a case-by-case basis.There are examples of calibrated parameters being included in a PSA. For instance, by taking a certain number of good-fitting parameter sets , bootstrCurrently, Bayesian calibration of microsimulation DMs might not be feasible on regular desktops or laptops. To circumvent current computational limitations from using Bayesian methods in calibrating microsimulation models, surrogate models -often called metamodels or emulators-have been proposed . SurrogaResearchers might actively avoid questions that would require HPC due to the perceived difficulties involved or make do with less-than-ideal smaller-scale analyses  and the robustness of the conclusions can suffer as a result.In this article, we showed that EMEWS could facilitate the use of HPC to implement computationally demanding Bayesian calibration routines to correctly characterize the uncertainty of the calibrated parameters of microsimulation DMs and propagate it in the evaluation of CEA of screening strategies and quantify their value of information. This study’s methodology and results could guide a similar VOI analysis on CEAs using microsimulation DMs to determine where more research is needed and guide research prioritization.


Osteonecrosis of the femoral head (ONFH) is caused when circulation within the femoral head is disrupted. Etiology of this disease is characterized by either traumatic events or atraumatic risk factors, such as chronic alcohol consumption or glucocorticoid use. ONFH commonly presents as pain in the groin, gluteus muscles, and/or knee with reductions in internal rotation range of motion of the hip. ONFH pathology can be confirmed with radiographic or advanced imaging and characterized by stage, size and location of the lesion. Treatment for ONFH consists of conservative and therapeutic methods as well as surgical intervention. Historically, ONFH has been treated using total hip arthroplasty (THA), but with increasing incidence in a younger, active population, measures to preserve the native hip joint have been explored. Recent advancements in hip arthroscopy and arthroscopy-assisted core decompression have led to improved outcomes, reduced pain and higher hip survival rate for early onset ONFH compared with more invasive approaches such as THA. Biologic treatments combined with arthroscopic core decompression have also shown improved outcomes and quality of life in few reports, suggesting a potential role for biologic adjuncts. The current study provides a comprehensive review and update on the literature surrounding arthroscopy-assisted core decompression for patients with ONFH. Osteonecrosis of the femoral head (ONFH) is characterized by apoptosis of osteoblasts and osteoclasts as well as mesenchymal and hematopoietic stem cells (responsible for generation of bone marrow) within the femoral head and neck, also deemed avascular necrosis (AVN). AVN is commonly caused by the interruption of blood supply to these specialized bone cells , 2. WithONFH is designated by two major categories: atraumatic and traumatic. Atraumatic ONFH is highly associated with alcohol and glucocorticoid use and is influenced by a number of other genetic, metabolic and physical blood flow restrictive factors , 5. TrauDespite increasing literature pertaining to the use of arthroscopic-assisted core decompression and decompression in conjunction with arthroscopic hip preservation surgery, a comprehensive evaluation of the literature is lacking. As these procedures continue to be performed, it will be imperative to assess the outcomes associated with these procedures as to critically evaluate and reassess the justification for their use. The purpose of the current study was to provide a comprehensive review and update on the literature surrounding arthroscopy-assisted core decompression for patients with ONFH.It is critical to perform a comprehensive history and physical examination in order to diagnose ONFH; in particular, questions should be directed at risk factors for OFNH in patients that present with hip pain, including history of alcohol and chronic steroid use, recent travel, medical and family history of genetic disease or Sickle cell anemia, history of trauma or irradiation and surgical history including procedures which may have jeopardized blood flow to the femoral head and neck. Pain is often reported in the groin, glute and/or knee with exacerbated pain in internal rotation of the hip .On physical examination, considerable loss of internal range of motion may indicate late stage ONFH and collapse of the femoral head . HoweverSeveral systematic reviews and meta-analyses have found the Ficat classification scale is most widely used for grading severity of ONFH followed by the University of Pennsylvania/modified Ficat (Steinberg) systems , 15. In Arthroscopic-assisted core decompression of the femoral head involves creating standard hip arthroscopy portals and performing a diagnostic arthroscopy. This allows the surgeon to identify other common intra-articular pathologies at the time of decompression, such cam-deformities, labral defects, and chondral defects . ThroughTheopold et al.  reportedn = 35) or percutaneous core decompression combined with selective arterial infusion . At a mean 30-month follow-up, the mean Harris Hip Score (HHS) was significantly greater in the experimental group compared with the control group , despite similar reported HHS preoperatively. They also reported that the change in the radiographic appearance of the femoral head was significantly better in the experimental group. Although both treatment methods are effective, the authors concluded that arthroscopic-guided core decompression can obtain better results as the necrotic femoral head can be positioned and scraped more accurately.Guo et al.  performeEllenrieder et al.  reportedGuadilla et al.  studied The above literature suggests that arthroscopic-assisted core decompression is a promising and efficacious treatment option in the treatment of pre-collapse stages of ONFH with or without additional intra-articular pathology. However, based on the paucity of literature and identification of only one randomized trial, there is currently only weak evidence to support its use and additional, high-quality trials are needed. In this current review, only one study had reported patient reported outcomes, the HHS at a mean 30-month follow-up. Future studies should incorporate patient reported outcomes at regular postoperative follow-up to determine subjective patient experience following arthroscopic-assisted core decompression.Arthroscopic-assisted core decompression for OFNH has several purported benefits including the ability to visual the joint surface to avoid penetration, perform diagnostic arthroscopy and address concomitant intra-articular pathology, and the potential for more comprehensive and accurate debridement of the avascular bone lesions. Based on the available literature, this procedure may effectively reduce pain and increase survivorship of the native hip in patients with pre-collapse ONFH prior, with better outcomes in earlier pre-collapse stages. There is inadequate evidence to recommend the use of biologic adjuncts such as platelet-rich plasma in conjunction with these procedures. Future research is imperative to continue to understand the indications and outcomes of arthroscopic-assisted core decompression.SS was the main author of this mini-review, performing the literature search and drafting/revising of this article. KK played a significant role in the revisions and writing of this manuscript as well as literature search for the final product. BN formulated and designed the outline of this mini-review and provided crucial editing and supervision to the final manuscript. All authors contributed to the article and approved the submitted version.BN declares ownership interest in BICMD (founder) outside of the scope of this manuscript. The remaining 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


SARS-COV 2 virus and the disease caused by the virus is a challenge for the healthcare workers since the beggining of the Covid-19 pandemic. Working with huge number of patient who need hospital care at the same time but also having scare information about the virus resulted with physical exhaustion, increased workload and mostly fear among the healthcare workers.The purpose of the study was to expand our knowledge about the mental health of the healthcare workers and explore most common psychological problems they faced during the Covid-19 pandemic over a period of three month.All participants in the study work in the same public hospital in Skopje, North Macedonia in one of the following job positions: physicians, nurses, paramedics, and hygienists. They anonymously filled out a google form answering questions about their mental health. Questions in the form were created based on Beck Depression Scale and the GAD-7 Scale for Anxiety along with questions about the gender, age, and job position.The results we gained from this study are showing that the participants experienced insomnia, reduced concentration, intense feelings of restlessness and fatigue, less energy and mostly lack of job satisfaction.Those symptoms indicates that healthcare workers developed anxiety and depression while working with patient infected by the SARS-COV 2 virus.No significant relationships.


Physical activity (PA) has been identified as an essential tool for the prevention and management of multi-morbidity in patients. Coordination of patients’ care through interventions like physical activity referral schemes (PARS) could foster the utilization of PA. This study explored the views of General Practitioners (GPs) and Exercise Physiologists (EPs) as key stakeholders, for optimizing patient care and efficiency of PARS. Sequential explanatory mixed methods design was used to explore the perceptions of these health professionals on PA and coordination strategies for PARS patient care. Data analyses included descriptive and inferential statistics for questionnaires and theoretical framework analysis for the semi-structured interviews. Participants demonstrated a good knowledge of PA and valued PARS. However, the findings unravelled external factors, inter-organisational mechanisms, and relational coordination obstacles that hinder efficient coordination of PARS patient care and delay/limit beneficial health outcomes for patients. Incentivising the PARS initiative and empowering patients to seek referral into the programme, are strategies that could boost PARS efficiency. Improving inter-professional relationships between GPs and EPs could lead to enhanced PARS functionality and efficient coordination of care for patients with chronic diseases. Globally, chronic diseases are the leading risk factors for disability and mortality . Three oHowever, current evidence calls into question the effectiveness of care coordination between health professionals. This is ascribable to time constraints, lack of knowledge, shared understanding of common goals and role clarity, cost implications and weak collaborations influenced by organisational culture and structure –18. For Coordination of care through interprofessional collaboration could be enhanced by adopting care frameworks focused on the promotion of teamwork, interprofessional channels and fostering the health and wellbeing of the populace , 25. A dTherefore, this mixed methods study employed an emerging care coordination framework to investigate the perceptions of Australian health professionals (GPs and EPs) regarding the coordination of care for PARS participants to determine effective ways to enhance the programme’s efficacy.The study sought to answer the following research questions (RQs):RQ1. What are Australian health professionals’ knowledge, beliefs, and attitudes towards PA and PARS?RQ2. What are Australian health professionals’ views regarding the coordination of PARS care for participants and how to optimize the programme?A sequential explanatory mixed methods design guided by a pragmatic approach  that incMixed methods design involves collecting, analysing and integrating of quantitative and qualitative data within the same study to answer specific research questions . CombiniThe ethical clearance for this study was secured from the Human Research Ethics Committee (HREC) of James Cook University (JCU) (Reference number: H7661). Designated health organisation representatives who assisted with participant recruitment were provided with the ethics approval details. Participants were further provided with the relevant information sheet, their privacy rights, and the possible benefits of the study. While keeping confidentiality and anonymity, electronic and verbal consents were sought from participants before the commencement of both phases of the study .a priori G-Power analysis [The first phase of the study answered RQ1 and utilised quantitative data collection and analytical techniques to examine GPs and exercise physiologists’ (EPs) knowledge, beliefs and attitudes about PA and PARS. An analysis  revealedA cross-sectional survey design was used to collect data from participants in this phase of the study. Issues identified from past PARS and care coordination studies informed the development and structure of the survey tool , 30. TheData were collected electronically via Survey Monkey® (by SVMK Inc.) between November 2019 and August 2020. Eligible participants were GP or EP, above 18 years and registered to practice in Australia. Participants were recruited via their work affiliations  and online fora including Twitter and Facebook. While GPs were recruited from clinical settings across Australia, EPs were mainly recruited via Exercise and Sports Science Australia (ESSA), the professional body for EPs. The first named author (FAA) facilitated and handled the correspondence for the recruitment application process. The process included the application and provision of an online survey link to participants or their affiliations. There was also an option of hard copies. To increase survey responses, reminder emails and incentives (a chance to win one of 10 $100 or five $200 gift vouchers) were used. Participants were assigned pseudonyms to protect their identity and were asked an optional question to request their participation in an interview.Data management and analysis were performed using IBM’s SPSS statistics software version 26. The survey data (including the pilot-tested data) were analysed using descriptive statistics , independent T-Test (for knowledge data) and Mann-Whitney U (for the belief data) statistical tests. Participants were categorised into groups (GP and EP groups) based on their professional affiliation. Results were displayed as frequencies and means ± SD and a p-value of ≤0.05 was considered significant.Participants who agreed to participate in the qualitative phase were purposively selected  to provide responses to RQ2. Semi-structured open-ended questions were then used to interview eligible participants between September and December 2020.A draft interview guide with open-ended questions based on the findings from the first stage of the study was pilot tested with five (5) participants (three EPs and two GPs) by the first author (FAA) and transcripts checked by another author (BSMA) to confirm the credibility and suitability of the questions. The findings from the pilot test were used to refine the interview guide . TelephoTen (10) semi-structured interview questions were used to explore participants’ views about coordinating PARS care for patients who utilized the programme’s services. Interview questions explored participants’ perception of their roles in coordinating PARS referrals for patients, PARS knowledge, beliefs and attitudes, influences of other health professionals (GPs or EPs), perceived challenges and benefits of PARS and their thoughts on how to improve the effectiveness of the patient care coordination for PARS. Prompts and probes were developed concerning the interview topics, when necessary, to kindle further responses from participants. Telephone interviews lasted between 16 and 50 minutes.nd interview after which three more interviews were conducted, totalling 25 interviews. Pseudonyms were assigned to respondents to aid anonymity.The interviewer (FAA) concluded each interview with a summary of interview accounts to secure trustworthiness and mutual understanding between both parties . Data saBefore data analysis, interviews were audio taped, transcribed verbatim and identity information removed. Interview transcripts (including those from the pilot test) were imported into QSR International’s NVivo version 12 for theoretical framework analysis. Framework analysis involves the screening, sorting, and charting of data based on crucial issues and themes . The ideFramework analysis  and the Two hundred and thirty-eight (238) participants (121 GPs and 117 EPs) responded to the surveys. Thirty-one (31) incomplete survey responses (19 from GPs and 12 from exercise physiologist—EPs) were excluded, while 207 completed responses (including 105 from EPs and 102 from GPs) were analysed. This response rate exceeds the required 64 participants per group required from the statistical power analysis.As portrayed in t (157) = -5.4, p <0.001, two-tailed). The magnitude of the difference in the means  was moderate (eta squared = 0.13).As shown in r = 0.85), U = 141.000, z = -12.289, p = .0001.Generally, EPs reported slightly stronger beliefs in PA and PARS value than GPs . When paAs displayed in Twenty-five (25) participants eight (8) GPs (32%) and 17 EPs (68%) participated in the individual telephone interviews. Participants included 14 males (56%) and 11 (44%) females. Qualitative findings were mapped unto the constructs of the care coordination theoretical framework. Based on the care coordination constructs, five overarching themes (1) External factors, (2) Patient knowledge and motivation, (3) (Inter)organizational mechanisms, (4) Relational coordination and (5) Outcomes were identified. Each theme is discussed below, and the representative quotes are presented in Participants highlighted the effects of some external factors which serve as obstacles to the effectiveness of the PARS programme. These obstacles included limited government support in terms of inadequate Medicare-funded CDM sessions which ultimately led to increased burden of cost (extra sessions) for patients. All participants perceived that PARS was undervalued by the government due to the few free CDM-funded sessions and Medicare rebates allocated to patients. The inadequate funding of PARS served as a barrier to the programme’s uptake and effectiveness. EPs reported that the limited funding for the programme compelled them to charge extra fees to compensate for the time they invest in patient care. They perceived that the undervaluing of their services ultimately impacts on continuity of care for patients who are unable to afford ongoing engagement with the PARS programme. GPs supported the EPs’ notion and indicated that the current five Medicare-funded sessions patients get to see any allied health professional of their choice are not enough and should be reviewed.An investigation of participants’ perceptions about PARS showed that motivating patients and providing adequate knowledge regarding PARS are essential for effective uptake of the programme. GPs expressed the importance of providing background PA and PARS knowledge to patients to help the patients appreciate and value the services of EPs, with subsequent better motivation and uptake of the referral. EPs substantiated the views of the GPs. Nonetheless, the EPs indicated that the patients were more knowledgeable than the GPs about PARS and often the patients were the ones providing information about PARS to GPs and proactively seeking referral into the programme.The participants expressed strong beliefs in the value of PARS and the need to coordinate care for patients through the programme. GPs spoke of the importance and need to collaborate with other HCPs. EPs substantiated the views of the GPs and emphasised the value patients attach to the involvement of GPs who help them achieve their health and wellness goals. However, poor visibility of EPs was identified by GPs as a major obstacle to the success of the PARS programme. They regarded the scarcity of EPs, particularly in regional and remote settings, as a critical factor that impedes the usability of the PARS programme. The limited availability was also reiterated by EPs who indicated that EP to patient ratio was low. Both GPs and EPs highlighted knowledge gap as another major obstacle to the success of the programme and this was attributed to poor information sharing about the benefits it has to offer. EPs also indicated that being time-poor, and overburdened with work, GPs might struggle to promote PARS to patients even if they have the information. In response to this, GPs faulted the PARS documentation process and time constraints as limiting factors for promoting PARS to patients.GPs and EPs had different approaches to patient care in relation to PARS. While GPs proposed PA and PARS interventions to patients and leave patients with the choice of uptake, EPs emphasised the importance of motivating patients and guiding them to see the benefits of taking up the intervention. This could be partly attributed to the lack of understanding of the roles and capabilities of EPs and how this affects patients’ ability to access the PARS programme. EPs were of the opinion that GPs were mostly unaware of the services that EPs offer. GPs admitted that they lacked understanding of the roles of EPs and perceived a need to clarify the boundary in the roles of EPs and other AHPs such as physiotherapists. Both participant groups indicated that an improved interprofessional relationship could be beneficial in the coordination of optimum care for patients. They stressed the need for feedback and information sharing to foster trust and improved functionality of the PARS programme. GPs indicated that they don’t receive feedback from EPs on the patients they refer to the EPs, while the EPs claimed that the GPs were not proactive enough in following up with the feedback from the PARS consultation. Instead, the feedback is often filed away by administrative staff, and this might prevent information from getting across to the doctors.Both participant groups reiterated the value of PARS in helping users achieve their health goals and regain the ability to perform their usual activities. GPs viewed collaboration with EPs as very essential and crucial to the improved wellbeing of the patients. The EPs emphasised the invaluable impact of shared experiences among PARS members. However, they expressed concerns about the delayed referral of patients to PARS and how this could make it difficult for the clients to achieve their health goals.The findings from both the quantitative and qualitative phases were synthesised and mapped to the themes of the care coordination framework. This mixed methods study employed a care coordination framework to explore the perceptions of key PARS stakeholders (GPs and EPs) regarding the coordination of care for PARS patients to determine effective and sustainable ways of fostering the health outcomes of patients and enhancing the effectiveness of PARS. Quantitative findings highlighted that GPs and EPs have good knowledge of PA and value PARS. Qualitative findings unravelled external factors, inter-organisational mechanisms, and relational coordination obstacles that hinder the ability to efficiently coordinate PARS patient care and delay/limit beneficial health outcomes for patients. These results substantiate our previous findings on the perspectives from patients  and uncoAn assessment of the external factors influencing the coordination of care by participants in the quantitative phase showed that barriers, including an undervaluing of the PARS programme and lack of financial incentive hinder the ability of GPs and EPs to coordinate patient care. A study by Clark et al.  revealedParticipants gave dissenting views on how the characteristics of patients influence the way they coordinate care. In the surveys, while GPs and EPs indicated strong beliefs in the value of PA for managing chronic conditions and praised the impacts of the PARS programme, they disagreed on the enthusiasm of patients to take up PARS initiatives. The interviews revealed that GPs and EPs value background PA and PARS education for patients before referral into PARS. EPs however, perceived that GPs were abdicating their frontline roles as PARS gatekeepers leading to patients initiating PARS referrals. A mixed methods study that explored the effects of empowering patients through self-management support, concluded that collaboration between patients and healthcare providers, access to self-management information and more diversified care for chronic diseases could optimise patient empowerment . TherefoExamining the inter mechanisms and relational coordination among health professionals revealed a complex coordination of care PARS process. Enhanced functionality of PARS would require further insights into the roles of EPs and improved accessibility to their services , 42. TheTo the best of our knowledge, this is the first study in an Australian context, that has used a care coordination model to explore the experiences of GPs and EPs regarding PARS. As key PARS stakeholders, the inputs of GPs and EPs would strengthen the evidence base on the coordination of care for PARS participants. Employing a sequential explanatory mixed methods approach ensured integration and in-depth understanding of the findings. However, the findings should be cautiously interpreted in the light of the following limitations: (1) The study considered only the perceptions of Australian GPs and EPs. (2) Although using a random sampling strategy facilitated the collection of information that could be useful for successful implementation of care coordination goals among health professionals, this strategy could have biased the responses of health professionals, as some respondents with affinity for PA and PARS could have been attracted to the study. (3) Finally, the results from this study were based on self-reported opinions of participants, which could have been either over- or under-estimated, owing to the specialty of health professionals who took part in the study.Health professionals’ views about care coordination for PARS participants have revealed desired outcomes. However, obstacles in most of the critical factors organisational mechanisms and relational coordination) facilitating the functionality of the care pathway limit the programme’s efficiency. Therefore, strategies that would promote GPs and EPs’ behavioural change towards effective care coordination are needed to foster quality care for patients, improve their health outcomes and forge a solid and efficient healthcare system.This study set out to critically appraise the views of GPs and EPs on the coordination of PARS care for patients to improve its efficiency and actively inform policy on PARS development or restructuring. Participants displayed good knowledge and firm belief in PARS, but health professionals, particularly GPs, require more knowledge, support, and incentives to promote, drive and coordinate PARS initiatives for patients effectively. Strategies to foster inter-professional relationships and efficient exchange of information between GPs and EPs are urgently required. This would enable insights into the roles and boundaries of PA specialists like EPs and unearth the values of the services they render. The findings from this research could inform policies that will enhance interest in PARS utilisation by frontline health professionals like GPs and the coordination of optimum care for patients, particularly those with multi-morbidity. A policy shift towards improving current incentives such as better PARS pay for health professionals and increased free EPC visits for patients could enhance positive mindsets and attitudes towards PARS initiatives among stakeholders. A broader view of all key PARS stakeholders, including GPs, EPs, and patients, concerning efficient ways to coordinate care for PARS participants could be invaluable to the initiative’s success.S1 Appendix(TIFF)Click here for additional data file.S2 Appendix(PDF)Click here for additional data file.S3 Appendix(PDF)Click here for additional data file.


Humans are capable of accurately judging their heading from optic flow during straight forward self-motion. Despite the global coherence in the optic flow field, however, visual clutter and other naturalistic conditions create constant flux on the eye. This presents a problem that must be overcome to accurately perceive heading from optic flow—the visual system must maintain sensitivity to optic flow variations that correspond with actual changes in self-motion and disregard those that do not. One solution could involve integrating optic flow over time to stabilize heading signals while suppressing transient fluctuations. Stability, however, may come at the cost of sluggishness. Here, we investigate the stability of human heading perception when subjects judge their heading after the simulated direction of self-motion changes. We found that the initial heading exerted an attractive influence on judgments of the final heading. Consistent with an evolving heading representation, bias toward the initial heading increased with the size of the heading change and as the viewing duration of the optic flow consistent with the final heading decreased. Introducing periods of sensory dropout (blackouts) later in the trial increased bias whereas an earlier one did not. Simulations of a neural model, the Competitive Dynamics Model, demonstrates that a mechanism that produces an evolving heading signal through recurrent competitive interactions largely captures the human data. Our findings characterize how the visual system balances stability in heading perception with sensitivity to change and support the hypothesis that heading perception evolves over time. Self-motion creates a streaming pattern of motion on the eye known as optic flow that contains rich information about the movement of the observer through the world . A prominditions . AlthougTo achieve stability in heading perception, the visual system faces a challenging problem: it must maintain sensitivity to the sensory flux that accompanies actual changes in self-motion while disregarding fluctuations that do not. An over-reliance on sensory signals could result in erratic, constantly shifting heading estimates, even when the observer’s heading does not actually change . In contOne solution could involve the accumulation of sensory evidence over time. Computational models may offer insight into how an evidence accumulation process when implemented in biologically plausible neural mechanisms could promote stability in heading estimation. However, many biologically inspired neural models of heading perception base their heading estimates exclusively on a single vector field representation of the optic flow field and therefore do not address the dynamics of heading perception over time . The ComSimulations of the CD model indicate that recurrent interactions among heading tuned neurons may play an important role in stabilizing heading perception, especially in dynamic environments wherein the observer moves in the presence of an independently moving object . Such moIn the present study, we investigated the stability of human heading perception from optic flow when the direction of self-motion changes. As There are at least two different ways in which the preswitch heading could influence judgments of the postswitch heading. One possibility is that the preswitch heading exerts an attractive effect, which means that the judged final heading is biased toward the initial heading relative to judgments of the final heading when the trial did not contain a switch b. The coThe aim of We independently varied the duration of the postswitch optic flow—250-ms or 500-ms. The CD model predicts that longer viewing time should improve the accuracy of heading judgments given that it provides more time for evidence to accumulate about the postswitch heading.To dissociate the influence of eccentricity on the judgment of the postswitch heading  from thaTwelve college students between the age of 18 and 21 years participated in the study for monetary compensation. All were naïve with respect to the purpose of the experiment, and had normal or corrected-to-normal vision. We obtained written consent from all participants before the experiment started. The experimental protocol was approved by the Institutional Review Board at Colby College and is in compliance with the Declaration of Helsinki.Subjects viewed displays  axis in the world that is aligned with the straight-ahead of the virtual observer. Simulated self-motion consists of pure translation within the present study , so the heading angle is the same in both world-relative and observer-relative reference frames. Trials that contained a switch in heading direction (switch condition) started with simulated self-motion along headings of 0The postswitch heading is the sum of the preswitch heading and the switch angle. We selected switch angles of eriphery .Subjects initiated each trial by clicking a mouse button. Each switch trial consisted of 750-ms of simulated translation along the preswitch heading, a switch in heading, and 250-ms or 500-ms of simulated translation along the postswitch heading. No-switch trials consisted of simulated translation along one of the heading directions used in the postswitch phase of switch trials. The duration was either 250-ms or 500-ms, matching the length of the postswitch phase of switch trials. Subjects were instructed to fixate and maintain gaze on a central fixation cross throughout the experiment. The last frame of the stimulus froze on-screen at the end of the trial and a thin yellow probe  of the experimental conditions in a fully crossed design. Each block consisted of 36 trials with a switch  and negative heading bias means bias away from the preswitch heading (repulsive effect). This was achieved by negating bias values associated with positive switch angles computed according to p values.When we used repeated measures analysis of variance (ANOVA) in statistical analyses of the bias data, we verified sphericity using Mauchly’s test. In cases where sphericity did not hold, we report Greenhouse-Geisser corrected degrees of freedom and The CD model is a biologically inspired, dynamical neural model of primate brain areas MT and MSTd that estimates both heading and object motion during self-motion  and builhttps://github.com/owlayton/NM-Competitive-Dynamics-Simple_Release.We briefly summarize the structure of the model of We simulated the model with sequences of analytically defined optic flow that resemble the conditions of o sample . TherefoAs shown in The next model stage corresponds with the area MSTd, where the spatially pooled output of model MT is matched against a set of optic flow templates tuned to radial expansion with different FoE positions  indicated that the bias with the 500-ms postswitch duration was significantly smaller than the 250-ms postswitch duration for all but the 3A key prediction was that extended viewing time of the postswitch optic flow should decrease heading bias owing to the preswitch heading. A paired samples, one-tailed Next, we examined the precision of postswitch heading judgments. Following ncreases d. There Because the duration of no-switch trials matched that of the postswitch period of Switch trials, the total duration of no-switch trials was 750-ms shorter than that of switch trials. We performed a control experiment with three subjects to examine whether the reduced trial duration in the no-switch condition could explain the influence of the preswitch heading. The design of the control experiment matched that of Humans generally underestimate the heading angle when judging heading from optic flow. This phenomenon is known as “center bias” because underestimation implicates error toward the center of the screen . Indeed,To more precisely quantify the predictions of the CD model and compare them with human heading judgments, we simulated the model with the conditions from Next, we examined the prediction that the accuracy of heading perception should improve over time. We quantified this by examining the model heading estimate at several simulation time steps before the end of each optic flow sample. Time steps closer to the end of the simulation correspond to longer times since the switch. Finally, we explored whether the CD model captures the mean precision of human heading judgments. As with the human judgments, we quantified the precision by computing the standard deviation of model heading estimates across repetitions of each condition. nditions f, h. Onenditions . SimulatIn summary, the CD model produces a pattern of heading bias that is similar to that of humans. Both human judgments and model estimates exhibit an attractive influence of the preswitch heading. Bias decreases when the postswitch optic flow appears for a longer period of time in the case of most of the switch angles tested. The model estimates exhibit greater precision than human judgments. Taken together, this shows that a mechanism that accumulates evidence about heading over time like the one in the CD model is capable of accounting for the human data.Thirteen college students between the ages of 18 and 21 years participated in the study for monetary compensation. All were naïve with respect to the purpose of the experiment, and had normal or corrected-to-normal vision. None of the subjects participated in The visual stimuli only differed modestly from those used in In the preblackout and postblackout conditions, the blackout removed 250-ms of optic flow from the preswitch and postswitch optic flow, respectively. By contrast, the trial duration was extended by 250-ms in the midblackout condition to account for the blackout period.The procedure was the same as in We used the same analysis as in We simulated 32 rather than the 34 frames of optic flow in the Given symmetric performance for left and right switch angles, we collapsed across switch angle sign. A three-way repeated measures ANOVA . Overall, however, we find the perception of a simulated saccade unlikely given that subjects maintained fixation during each trial and the visual system would need to tolerate a conflict between stationary position of the real eye and the shift in the optic flow.It is also possible to experience a shift in the singularity position while moving along a curvilinear path. Curvilinear self-motion under naturalistic conditions often involves concomitant rotation of the body and gaze around the curved path that gives rise to a combination of translational and rotational optic flow . HoweverThe influence of previously viewed stimuli on human perceptual judgments occurs widely through what is known as serial dependence . The typsame trial rather than across consecutive trials. We used this approach to address the stability of heading perception during a continuous period of simulated self-motion. Although the effect of the preswitch heading on heading judgments that is demonstrated in the present study could be considered “serial dependence”, we emphasize that we used a paradigm that is different from that commonly used in the serial dependence literature. In particular, we examined how a change in heading direction influences heading judgments within the The correspondence between the CD model estimates and the human data in the present study raises the possibility that similar neural mechanisms could underlie heading perception when the direction of self-motion changes. h 250-ms b. By 833Neural adaptation represents another possible mechanism, though it acts in diverse ways depending on the stimulus and brain area. For example, adaptation may exert either attractive and repulsive neural effects in different circumstances . This ma


Performance modeling of hospitals using data envelopment analysis (DEA) has received steadily increasing attention in the literature. As part of the traditional DEA framework, hospitals are generally assumed to be functionally similar and therefore homogenous. Accordingly, any identified inefficiency is supposedly due to the inefficient use of inputs to produce outputs. However, the disparities in DEA efficiency scores may be a result of the inherent heterogeneity of hospitals. Additionally, traditional DEA models lack predictive capabilities despite having been frequently used as a benchmarking tool in the literature. To address these concerns, this study proposes a framework for analyzing hospital performance by combining two complementary modeling approaches. Specifically, we employ a self-organizing map artificial neural network (SOM-ANN) to conduct a cluster analysis and a multilayer perceptron ANN (MLP-ANN) to perform a heterogeneity analysis and a best practice analysis. The applicability of the integrated framework is empirically shown by an implementation to a large dataset containing more than 1,100 hospitals in Germany. The framework enables a decision-maker not only to predict the best performance but also to explore whether the differences in relative efficiency scores are ascribable to the heterogeneity of hospitals. A novel framework for homogeneity and best practice analyses of hospitalsCombining DEA with artificial neural networks for clustering and homogeneity analysisStudy the influence of heterogeneity of hospitals on the relative efficiencyBest performance predictions that reveal a large potential for improvementSupporting managers in designing a stepwise efficiency improvement planThe Federal Statistical OfficeIn the DEA context, homogeneity of a set of DMUs means that all DMUs operate in the same environment and pursue the same target with the same processes. Although significant research has been conducted on the heterogeneity of DMUs, many studies utilize the homogeneity assumption as pointed out by Haas and Murphy  and WojcThe impact of the hospital environment can be modeled implicitly by grouping similar DMUs to their transformation capacity (or technology) together. This requires a technique that uncovers categories in the large and multidimensional dataset of DMUs. Incorporating environmental variables in DEA studies has traditionally relied on the two-stage model  for hospital performance evaluation despite the established effectiveness of these approaches  the transformative capacity model (TCM) to analyze the homogeneity, and (ii) the best practice model (BPM) to predict the level of improvement desired, to achieve efficient operation. The rest of the paper follows this structure. In Section 3, we describe the research methodology and the multi-stage analytical framework combining SOM-ANN, SBM DEA, and MLP-ANN. The dataset of German hospitals used to demonstrate the framework’s applicability is presented in Section 4. The results of the implementation of the framework are presented in Section 5. Section 6 concludes with a discussion of future research directions and conclusions.In this section, we describe our proposed framework  between the SBM DEA estimates and bootstrapped DEA estimates produced by implementing the algorithm developed in Daraio and Simar  with theA DEA estimator of the production frontier is a fully-fledged statistical methodology Banker  by whichi) MLP-ANNs have a stochastic learning process, which minimizes the chance of being trapped in local minima, and (ii) there is no necessity to specify and know the relationships within the dataset. This architecture, the multilayer feedforward network, is mostly used with the backpropagation algorithm.In this stage, two MLP-ANN architectures are designed in two different ways, which are explained in detail in the following subsections. The first architecture supports the scale heterogeneity analysis and the second one is used to predict the actual output level necessary for an inefficient hospital to be efficient. The MLP-ANN maps complex unknown relationships in the dataset because  whether the relative efficiency score of hospitals in a certain cluster improves if we consider the TCM of other clusters, and (ii) identifying the differences that are partially due to scale heterogeneity. The second part of the analysis aims at exploiting the non-linear mapping capabilities of MLP-ANN by using the input and output data of each cluster as input nodes (input layer) and assigning their efficiency scores received from DEA-SBM as target nodes (output layer). We develop both MLP-ANNs using an end-to-end open-source platform called TensorFlow in Python 3.8. We set the mean absolute percentage error (MAPE) as the performance measure due to its scale independence, interpretability, and simplicity. For training, validation, and testing, we use a random data division function. The training function updates weight and bias values based on “Adam”, a stochastic optimization method developed by Kingma and Ba  of this cluster to the TCMs generated for Cluster 2 . Results are reported in Table Similar to the first procedure, our next step is to find the best settings for the newly designed MLP-ANNs  for our best practice analysis of hospitals. The performance measure of the trained BPMs is reported in Table The frontier function can be viewed as the upper limit of the support of the density of hospitals in the input and output space. On the efficient frontier, concavity and monotonicity assumptions are assumed to be preserved by DMUs. However, the bootstrapped estimates do not necessarily preserve the concave monotone increasing condition. As a result, BPMs are trained based on the SBM DEA estimates where concave monotonic properties of the efficient frontier are preserved  would not reveal homogeneity within groups of hospitals, nor would it identify heterogeneity between groups of hospitals.According to the SBM DEA estimates, the distribution underlying the bootstrapped DEA estimates is identical to the distribution underlying the SBM DEA estimates.The differences in the relative efficiency of some German hospitals can be due to differences in their transformation capacities rather than inefficient input usage in producing outputs. Furthermore, a part of the reason for the disparities between the relative efficiency scores of hospitals is scale heterogeneity.The trained BPMs can compensate for the lack of predictability of standard DEA models due to their nonlinear mapping and adaptive prediction abilities.The framework examines the hospital dataset that the Federal Joint Committee of Germany recorded in 2017. To ensure complete accuracy and robustness in calculations, many preprocessing steps are involved in each stage of the framework due to the vast and complex dataset. The proposed framework possesses improved prescriptive capabilities over DEA approaches in a heterogeneous environment. The developed framework may also contribute to the creation of continuous improvement opportunities by promoting the best management practices within a group of hospitals. The proposed framework advances the current benchmarking paradigm of hospitals by learning the optimal performance pattern of hospitals on the efficient frontier of each group. By using what-if and identifying improvement scenarios, the framework can assist decision-makers in evaluating efficiencies. There are clearly defined stages in this study’s framework, and different methods are employed as part of each stage. Analyzers can address the effect of environmental variables on heterogeneity without adding additional variables to DEA models. The key findings of this study can be summarized as follows:Most studies ignore the heterogeneity pitfall even though it is widely recognized that DEA studies can be compromised by it. DEA would be more robust if methods were developed to prove the reliability and correctness of results. DEA models alone cannot resolve the major problems in hospital performance management that arise from operating in an environment heterogeneous in nature. Because exogenous factors are complex and multiplicative, identifying and measuring them is challenging. Consequently, the process of selecting a reference set for every hospital should be handled cautiously. As demonstrated by well-established quality indicators, it is interesting to note that, contrary to previous findings (Tiemann et al.


In modern smart orchards, fruit detection models based on deep learning require expensive dataset labeling work to support the construction of detection models, resulting in high model application costs. Our previous work combined generative adversarial networks (GANs) and pseudolabeling methods to transfer labels from one specie to another to save labeling costs. However, only the color and texture features of images can be migrated, which still needs improvement in the accuracy of the data labeling. Therefore, this study proposes an EasyDAM_V2 model as an improved data labeling method for multishape and cross-species fruit detection. First, an image translation network named the Across-CycleGAN is proposed to generate fruit images from the source domain (fruit image with labels) to the target domain (fruit image without labels) even with partial shape differences. Then, a pseudolabel adaptive threshold selection strategy was designed to adjust the confidence threshold of the fruit detection model adaptively and dynamically update the pseudolabel to generate labels for images from the unlabeled target domain. In this paper, we use a labeled orange dataset as the source domain, and a pitaya, a mango dataset as the target domain, to evaluate the performance of the proposed method. The results showed that the average labeling precision values of the pitaya and mango datasets were 82.1% and 85.0%, respectively. Therefore, the proposed EasyDAM_V2 model is proven to be used for label transfer of cross-species fruit even with partial shape differences to reduce the cost of data labeling. High-performance visual perception systems (as a key technology for automated fruit operation system in orchards) can be applied to smart orchard such as fruit positioning , 2, orchIn our previous research , a crossWith the rapid development of GAN   technoloTherefore, some scholars have also researched effectively translating the object shape feature under unsupervised GAN conditions. Wu et al.  proposedIn addition, during the use of the current pseudolabel method –29 for oTo address the translation problem for the different species of fruit images with partial shape differences, this paper proposes a new unsupervised fruit image translation network called Across-CycleGAN. The Across-CycleGAN does the following: it enhances the extraction ability of global shape features by introducing a multilayer feature fusion strategy in the generator network. Further, the paper proposes a multidimensional feature loss function that uses a cross-cycle loss function comparison path to learn the shape feature of fruit images interactively, effectively trains the network to learn shape feature differences between different species of fruit images, and generates synthetic fruit images suitable for deep learning detection model trainingTo address the problem of manually determining the confidence threshold in the pseudolabel process, this paper proposes a pseudolabel adaptive threshold selection strategy. This strategy measures the quality of the generated label data under different confidence thresholds and adaptively calculates the corresponding optimal confidence threshold for the pseudolabelTherefore, we propose EasyDAM_V2 to achieve efficient label conversion between different species of fruit datasets with partial shape differences. In this method, based on the original CycleGAN , we propThe flowchart of the proposed EasyDAM_V2 is shown in First, we input the labeled source domain fruit dataset, translate the source domain fruit image to the target domain synthetic fruit image by the fruit image translation model (in the image generation module), and construct the labeled target domain synthetic fruit dataset (by combining the label data of the source domain fruit dataset). The fruit detection model OrangeYolo  proposedSecond, the unlabeled target domain actual fruit images are input into the constructed pretrained fruit detection model for the target domain. Then, the adaptive threshold selection strategy is used in the label generation module to select the optimal initial confidence threshold for the fruit detection model, and finally, it obtains the target domain actual fruit image pseudolabel . In the label generation module, this paper proposes a pseudolabel adaptive threshold selection strategy , and the fruit image resolution was uniformly adjusted to a size of 256 × 256 to facilitate the input of the fruit image translation model for training. orange&pitaya dataset: the dataset mainly contained fruit images of two species, orange and pitaya fruits. The training set contained 980 orange fruit images and 416 pitaya fruit imagesorange&mango dataset: the dataset mainly contained fruit images of two species, orange and mango fruits. The training set contained 980 orange fruit images and 141 mango fruit imagesIn the training process of the fruit image translation model Across-CycleGAN, two datasets, Source domain orange dataset: the dataset follows the orange fruit dataset in the object detection dataset section of the EasyDAM  researchTarget domain fruit datasets: contained the pitaya and mango datasets: target domain pitaya datasets: the data was collected from an orchard in Beijing, China, using a Samsung Galaxy S8 phone  while the pitaya images in the orchard were additionally collected online and integrated. The dataset contained 377 pitaya images; the training set contained 265 unlabeled pitaya images, and the test set contained 112 labeled pitaya imagesIn fruit detection, fruit images from actual orchard scenes were used to produce labeled fruit datasets for the subsequent method validation. The sample images of the fruit detection dataset are shown in Target domain mango dataset: the dataset used the mango dataset published in research . The fruS and the target domain T. The Across-CycleGAN mainly includes two generator networks: GS−>T and GT−>S; and two discriminator networks: DS and DT (which aim to learn the image distribution mapping relationship between different image domains).The proposed Across-CycleGAN can be applied to fruit image translation with partial shape differences. The model training flow chart is shown in In this paper, we propose a multilayer feature fusion strategy in the generator network to fuse the image feature of the deep and shallow network layers to enhance the network to learn the shape feature of the fruit image.We use the image translation process from the source domain to the target domain as an example. In the generator network of the original CycleGAN shown in , the souMeanwhile, the discriminator network follows the PatchGAN  in the oLCycle) and identity loss function (LIdentity) are used to ensure that the network can migrate the color and texture features between different species of fruit images under unsupervised and unpaired datasets conditions. At the same time, the adversarial loss (LAdv) is used to stabilize the network training effect and generate higher quality synthetic fruit images.In this paper, we propose introducing shape feature loss functions based on the original CycleGAN and constructing multidimensional feature loss functions for color, texture, and shape features (to train the fruit image translation network). In the original CycleGAN shown in , a combiLSF) and then a multidimensional feature loss function constructed to optimize the network  Cycle consistency lossFor the fruit shape feature loss function design, this paper proposes to introduce the shape feature loss function Equation . The losLCycle in the original CycleGAN is followed, which can effectively preserve the original image content information by limiting the size of the image distribution mapping space in different image domain translation directions. The correlation loss function is described by Equation ((3) Identity lossThe cycle consistency loss function Equation  as folloLIdentity is used to train the network to learn a single mapping relationship of the image distribution in different image domain translation directions. This is done in order to enhance the ability of the network to preserve image color and texture features. The correlation loss function is described in Equation ((4) Adversarial lossThe identity loss function Equation  as folloLAdv in the original CycleGAN is followed, including two parts of loss functions LAdv , with the aim of stabilizing the network training effect and obtaining higher quality generated image results. The adversarial loss function LAdv is calculated as follows, as well: i.e., LAdv:The adversarial loss function Equation ) and LAdGS−>T and GT−>S) and discriminator networks (DS and DT), the different feature loss errors in two independent and different domain cycle directions  are calculated. However, during each domain cycle, for example, domain cycle 1: S > T > S, the domain cycle (shown in T > S > T).In the original CycleGAN, based on two generator networks , as shown in In deep learning object detection tasks, the pseudolabel (similar to human-like data labeling) is automatically generated by the detection model, replacing the manual labor of labeling and effectively reducing the dataset labeling effort. The accuracy of the generated pseudolabel depends not only on the detection performance of the model but also on the size of the confidence threshold set by the detection model. When the confidence threshold of the model is set to a low threshold, the detection box retention condition is loose, and the number of acquired pseudolabels is high. This is to comprehensively cover the foreground object area in the image and provide more correctly labeled boxes for the detection model to train and retain more noisy label data , i.e., in the case of pseudolabel generated under high confidence threshold conditions. The confidence threshold of the model is then analyzed in the process of pseudolabel generation. It is often necessary to find a balance point threshold in the process of setting the confidence threshold, which can make the generated pseudolabel balanced in terms of the quantity and confidence score (reduces the influence of noisy labels). However, in pseudolabeling methods proposed in previous research (EasyDAM ), a manuR ∈ , and obtain the total number N and the average confidence score μ of the pseudolabel within the confidence threshold range RSet the confidence threshold range R, iterate the different confidence threshold values at intervals of 0.01 and note the current confidence threshold value as Conf.In the confidence threshold range N0 and μ0, respectively. Calculate the corresponding percentage of the number of pseudolabels w0 = N0/N.Count the confidence scores of the pseudolabels less than Conf. The number of pseudolabels and average confidence scores are denoted as N1 and μ1, respectively. Calculate the corresponding percentage of the number of pseudolabels w1 = N1/N.Count the confidence scores of the pseudolabels greater than Conf. The number of pseudolabels and the average confidence scores are denoted gConf of the pseudolabel under the corresponding confidence threshold condition according to the following equation: gConf = w0(μ0 − μ)2 + w1(μ1 − μ)2.Calculate the quality variance value Repeat steps (2)~(5) to count the variance values of the pseudolabel quality under each confidence threshold, and finally, select the confidence threshold corresponding to the maximum variance value as the best confidence threshold of the pseudolabel methodTherefore, this paper proposes an adaptive threshold selection strategy for pseudolabel generation methods by combining the fruit detection model OrangeYolo  proposedIn the above method, the impact of differences in the quantity and score information of the generated pseudolabel under different confidence thresholds is measured, and the confidence threshold of the fruit detection model is adjusted dynamically. This enables to effectively reduce the negative impact of noisy labels in the subsequent fine-tuning of the fruit detection model. Meanwhile, this pseudolabel method can reduce a large number of comparison test work under different confidence thresholds, which improves the efficiency of the pseudolabel method application and obtains a high-precision pseudolabel.F1 score, and average precision (AP) metrics. Higher values of the corresponding indexes indicate better detection model performance. Among them, the precision, recall, and F1 score are taken from the model performance balance point  and the details of the evaluation methods can be found in the previous work [To verify the accuracy of the labels generated from the target domain actual fruit dataset, the effectiveness of the proposed method was indirectly verified by the performance of the target domain fruit detection model obtained from the final training. The evaluation methods of target domain fruit detection models mainly use the precision, recall, ous work .Mpitaya and Mmango, respectively (introduced in Based on the fruit image translation network (Across-CycleGAN), the target domain pretrained pitaya and mango fruit detection models were constructed, denoted as orange2pitaya experiment and orange2mango experiment, respectively . We indirectly verified the effectiveness of the target domain synthetic fruit images generated by the Across-CycleGAN through the performance of the constructed target domain pretrained fruit detection model.In this section of the experimental work, on the basis of the labeled source domain orange dataset, the Across-CycleGAN was used to produce the labeled target domain synthetic pitaya and mango datasets, which were applied to construct the target domain pretrained pitaya and mango fruit detection models   and counMpitaya (constructed based on the Across-CycleGAN), an AP value of 72.8% and an F1 score value of 71.0% were obtained from the test in the target domain of actual pitaya images. In the two-test metrics: the AP and F1 scores of the Across-CycleGAN test results improved by 5.3% and 3.7% as compared to that of the U-GAT-IT network [Mmango (constructed by the Across-CycleGAN) was tested on the target domain of actual mango images in real scenes to obtain an AP value of 68.7% and an F1 score value of 67.8%, which were better than the experimental results of other images translation algorithms. network . It also network . MeanwhiMpitaya. The AP value only reached 11.0%. Meanwhile, it was observed in the target domain synthetic mango images acquired by the Council-GAN [Mpitaya could be guaranteed to detect the actual pitaya images (based on the color and texture features). Meanwhile, in the target domain synthetic mango images obtained by the U-GAT-IT network [Mmango to still have preliminary mango fruit detection performance.ncil-GAN  that thencil-GAN , althoug network , the colFinally, the results of different species of synthetic fruit images generated by the proposed Across-CycleGAN showed that the network could achieve the translation of foreground fruit color and texture features (while minimizing the distortion in the background region of the image). It also got a certain degree of translation in the shape features, bringing accuracy improvements with the best performance.To further validate the effectiveness of each improved strategy in the proposed Across-CycleGAN, including the multilayer feature fusion strategy (MFFS) and the multidimensional feature loss function (MFL), relevant ablation experiments were conducted in this study. As shown in the experimental results in Meanwhile, we generated several other species of target domain fruits to validate the Across-CycleGAN effectiveness, with orange as the source domain fruit (containing 980 images) and pear, kiwi, and green pepper as the target domain fruits . The fruit images in the dataset were searched from the Internet (without copyright restrictions). As shown in Mpitaya and Mmango) obtained based on the Across-CycleGAN construction, this study proposed a pseudolabel adaptive threshold selection strategy to further obtain the pseudolabel of the target domain actual pitaya and mango datasets, respectively (denoted as the orange2pitaya and orange2mango experiments).In the target domain pretrained fruit detection models  and the pseudolabel self-learning method (denoted as PL-SL) under different confidence threshold conditions, respectively . It is also 3.5% higher than that of the best experimental results of the pseudolabeling self-learning method PL-SL . In the orange2mango experiment, the initial optimal confidence threshold of the target domain pretrained mango detection model was 0.48 and the generated label data of the target domain actual mango dataset achieves AP and F1 score values of 85.0% and 81.7%, respectively. Compared with the traditional pseudolabeling method T-PL  and the pseudolabel self-learning method PL-SL , the AP values of our model improved by 1.2% and 0.5%, respectively, which are both better than the comparison algorithm experiments.orange2pitaya experiment, the target domain pitaya fruit showed irregular shape, which was different from the source domain orange fruit with a nearly round shape. The fruit images in the source and target domains are collected from two scenes: an outdoor orchard and an indoor greenhouse, respectively. From the experimental results, it is clear that for the task of the label conversion of fruit datasets with partial shape differences and large differences in scenes (indoor and outdoor), the label data generated by the proposed method in this study could correctly label the foreground pitaya fruit region of the image on a larger area. Meanwhile, in the orange2mango experiment, the target domain actual mango images were collected from the dark night environment when compared with the source domain orange images collected from a daytime environment. There were great differences in image scenes and illumination brightness. The method proposed in this study could achieve higher accuracy in the label conversion of the fruit dataset, which verifies the effectiveness of the proposed method.Finally, the label data of the target domain actual pitaya and mango images generated by the proposed method in this study are visualized .In the research of the Across-CycleGAN (the fruit image translation network), improvements were mainly made in both the network structure and loss function, which can be applied for translating different species of fruit images with partial shape differences. It was also compared with the current advanced image translation algorithms: the U-GAT-IT network  and CounIn the pseudolabel adaptive threshold selection strategy proposed in this study, the strategy combines the performance of the target domain pretrained fruit detection model and the image complexity of the unlabeled target domain fruit images . The proposed pseudolabeling method was compared with the traditional pseudolabel and pseudolabel self-learning methods under different confidence threshold conditions. Meanwhile, the pseudolabel method proposed in this study mainly searches for the optimal confidence threshold balance points by measuring the quantity and score information characteristics of the pseudolabel. In order to reduce the negative impact of noisy pseudolabeling during the fine-tuning of the target domain fruit detection model, further filtering operations are required for the actual noisy label data in the generated pseudolabel. In addition, the target domain pretrained fruit detection model is mainly trained by the target domain synthetic fruit dataset. Both the synthetic fruit images and actual fruit images in the target domain have certain differences in the scale of the fruit, which may lead to the target domain pretrained fruit detection model in the pseudolabel generation process. Since the pseudolabel is more difficult to label the complete area of the foreground fruit accurately, this will result in the mislabeling phenomenon. Another future improvement in this study is to further solve the mislabeling phenomenon caused by the difference in fruit image scales between different domains.In summary, the cross-species fruit dataset label conversion method proposed in this study for converting labels of different species of fruit datasets with partial shape differences can effectively solve the high cost of labeling fruit datasets problem. Meanwhile, in the application of modern intelligent orchards, according to the actual fruit detection task requirements, the method of this study can efficiently generate fruit image labels of the required species in the target task and quickly build a high-precision fruit detection model (solving the problem of complicated dataset labeling in the current deep learning fruit detection technology). It can be further equipped with relevant agricultural machinery and equipment applied to other intelligent orchard work to improve the intelligent efficiency of the orchard.