ObjectivesNeuraxial anesthesia is the preferred anesthesia technique for cesarean delivery due to a decreased risk of adverse events. However, general anesthesia is often employed during emergent cesarean deliveries to achieve a shorter decision-to-delivery interval. The objective of this study was to determine if the conversion of epidural labor analgesia to surgical anesthesia for a category-1 cesarean delivery is associated with significant neonatal morbidity.Study designThis was a retrospective cohort study of all intrapartum category-1 cesarean deliveries performed at an academic tertiary care institution between August 2016 and July 2021. The primary outcome was neonatal morbidity, defined as a composite of neonatal umbilical artery pH < 7.10 and/or 5‐min Apgar score < 7, and/or neonatal intensive care unit admission. A multivariate regression analysis was performed to control for the presence of covariates and examine the degree to which they influenced the outcome.ResultsA total of 185 mother-neonate pairs qualified for inclusion, of which 23 had cesarean delivery under general anesthesia and 162 under epidural anesthesia. There was no significant difference in adverse neonatal outcomes between category-1 cesarean deliveries done under general anesthesia compared to epidural anesthesia . The incidence of umbilical arterial pH < 7.10 was higher in the general anesthesia group compared to the epidural anesthesia group . The multivariate regression model showed that gestational age  and non-reassuring fetal heart trace  were significant predictors of adverse neonatal outcome.ConclusionOur results suggest that the conversion of epidural analgesia to surgical anesthesia for category-1 cesarean delivery in women with a functional labor epidural catheter is not associated with poorer neonatal outcomes. Emergency cesarean delivery (CD) is required when there is an immediate threat to the life of the fetus or mother. To expedite delivery, there must be clear, unambiguous communication between the obstetric, nursing, and anesthesia teams caring for the patient. In 2016, the obstetric unit at our institution adopted the classification system for the urgency of CD first proposed by Lucas et al. and standardized by the Royal College of Obstetricians and Gynaecologists (RCOG) in the United Kingdom . The timStudy designThis was a retrospective cohort study of all intrapartum category-1 cesarean deliveries performed at the University of Iowa Hospitals and Clinics between August 2016 and July 2021. The study was approved by the University of Iowa Institutional Review Board (IRB # 201911151), and a waiver of consent was obtained before data collection. The electronic medical record was queried for all CDs during the study period, and individual chart reviews were performed to determine study eligibility. Study data were collected and managed using Research Electronic Data Capture (REDCap) tools hosted at the University of Iowa. We included all CDs classified as category-1 in which epidural analgesia was established in labor before the decision to deliver emergently. Cesarean deliveries were excluded if there was documentation of downgrading of urgency after the patient's arrival in the operating room. To determine if the urgency of delivery was downgraded before the surgical incision, we utilized the operating room entry to skin incision interval (ORII) as a reference. Operating room-to-skin incision interval (ORII) served as the most reliable way to determine the acuity of urgent delivery, as a valid category-1 CD would have a short ORII. Cases with ORII of > 10 minutes were deemed to have been downgraded in the level of urgency and excluded from the analysis. A previous study reported a significant effect of the urgency of CD on the ORII and has been suggested as an accurate surrogate for the urgency of delivery . The parManagement of labor analgesia and anesthesia for category-1 CDIn our obstetric unit, we offer a combined spinal epidural (CSE) analgesia technique for labor analgesia unless contraindicated. When the obstetric team decides to perform a category-1 CD, it is communicated via the emergency paging system, and the patient is immediately transferred to the operating room. In women with functional epidural catheters, a bolus of 3% Chloroprocaine 20 mL is administered via the epidural catheter on route to or on entry to the operating room. Alternatively, a mixture of 2% Lidocaine with epinephrine 1:200,000 is administered in up to four 5 mL aliquots for a total bolus dose of 20 mL. Hemodynamic monitoring is established, and the abdomen is prepared and draped. Before a surgical incision is made, the sensory block level is assessed. If a sensory block to the T4 dermatome is not achieved, rapid sequence induction of GA with propofol (2-2.5 mg/kg) and succinylcholine (1 mg/kg) is performed. However, depending on the scenario or following specific requests from the obstetrician, GA may be induced without attempting to top up the epidural catheter. Non-invasive blood pressure (NIBP) measurements are recorded at 1-minute intervals once the epidural top-up is given, or GA is induced until delivery and every 2.5 minutes subsequently. A prophylactic phenylephrine infusion is administered at a rate of 0.25-0.75 mcg/kg/min  for the management of hypotension.Statistical analysisDescriptive statistics were used to summarize demographic characteristics and neonatal outcome variables stratified by anesthesia type. Categorical measures are reported as counts and percentages. Continuous measure distributions were assessed for normality using the Shapiro-Wilk test. Normally and non-normally distributed measures are reported as means and standard deviations or medians and interquartile ranges, respectively. Tests for differences between the anesthesia types were performed using Wilcoxon rank sum tests for continuous measures and Fisher’s Exact Test for categorical measures. The primary outcome for this study was neonatal morbidity, defined as a composite of umbilical artery pH <7.10 and/or 5‐min Apgar score <7, and/or NICU admission. A multivariate regression analysis was performed to control for the presence of other covariates and examine the degree to which they influenced the outcome. This model was initially fit with anesthesia type, ORII, and the interaction between these two variables as the predictors. Then, all variables from the candidate pool of additional variables were investigated, and the variable that most greatly reduced the Akaike information criterion (AIC) was added to the model. This process was repeated until no more additions from the candidate pool of covariates reduced the model’s AIC. The candidate pool of additional covariates consisted of gestational age, birth weight, induction of anesthesia to delivery time, maximum drop in systolic blood pressure, and indications for CD. Any candidate variables that caused convergence issues with the model due to their low prevalence in our study were excluded from consideration for the multivariate model. All logistic modeling is reported as odds ratio estimates, 95% confidence intervals, and p-values for all covariates. All tests were conducted using an alpha = 0.05 significance level. Univariate logistic models assessing the impact of anesthesia type were constructed for each of our secondary outcomes of interest, which include umbilical cord arterial pH, umbilical cord arterial base deficit, NICU length of stay, DDI, and ORII. For all binary outcomes, receiver operator characteristic (ROC) curves were generated to assess the relationship between the ORII and each of the outcomes of interest. Optimal cutoff value, sensitivity, specificity, and area under the curve (AUC) were recorded for each outcome. A sensitivity analysis was performed excluding patients that had a conversion of EA to GA. This was done to confirm that this subgroup of patients was not driving the findings of our primary analysis.A total of 3,869 cesarean deliveries were performed between August 2016 and July 2021, accounting for 32.6% of all deliveries. Two hundred and ten mother-neonate pairs met enrollment criteria. We excluded 25 women because the urgency of CD was downgraded on arrival in the operating room. A total of 185 mother-neonate pairs; EA group =162 and GA group = 23 were included in the final analysis . The multivariate regression model showed that gestational age  and NRFHT  were significant predictors of the composite adverse neonatal outcome. The summary of the multivariate regression model is shown in Table The result of our study indicates that the conversion of epidural labor analgesia to surgical anesthesia for category-1 CD in women with functional labor epidural catheters is not associated with poor neonatal outcomes. The multivariate regression model indicated that after adjusting for the longer ORII and DDI associated with EA, the gestational age of the newborn and non-reassuring fetal heart trace (NRFHT) were the only significant predictors of neonatal morbidity. Several studies have investigated the effect of anesthesia techniques for emergent CD on neonatal outcomes. A study conducted at a tertiary care obstetric unit in France reported that GA was associated with an increased incidence of neonatal respiratory interventions, transfer to the neonatal intensive care unit, and neonatal mortality . A similOur data showed that cord prolapse was the indication for CD more likely to be performed under GA. This is not unexpected considering that a GA is associated with shorter DDI and the need for immediate delivery. However, a significant number of CDs performed for cord prolapse in our cohort were done by topping up the indwelling labor epidural catheter to achieve surgical anesthesia. Therefore, it is reasonable to attempt using neuraxial anesthesia for CD in women with functional indwelling labor epidural analgesia catheters, even in these critical scenarios. There is concern that attempting neuraxial anesthesia for category-1CD and subsequently converting to GA if unsuccessful may be associated with a higher risk of neonatal morbidity versus proceeding with a GA initially. A study comparing neonatal morbidity of emergency CD performed following the conversion of neuraxial anesthesia to GA with those performed under GA with no prior attempt to use neuraxial anesthesia found no difference in neonatal outcomes .However, more extensive prospective studies are required to confirm this finding. Notably, our study found a 32% conversion rate from EA to GA. Conversion from EA to GA was not associated with worse neonatal outcomes, but our rate is higher than reported in the literature. Most studies have reported on the conversion rate for all categories of CDs, not solely category-1 . In a stThe overall rate of GA use for category-1 CD at our institution is slightly lower than that reported in the literature for an obstetric unit with a similar delivery volume . This isThe limitations of our study include the retrospective design, which is subject to selection and information bias. A CD initially designated as a category-1 could likely have been downgraded to a category-2 on arrival in the operating room, allowing adequate time to achieve surgical anesthesia using the epidural catheter. These scenarios are often chaotic, and the downgrading of the level of urgency may not always be documented accurately in the medical records. Umbilical arterial blood gas data were unavailable for thirty-one women, which could have potentially introduced bias to our outcomes. Additionally, the study consisted of a relatively small sample size and an uneven distribution between GA and EA groups. Future prospective multi-center studies may overcome these limitations. Finally, considering that we offer CSE for labor analgesia to most of our parturients, the findings in this study may not be generalizable to parturients whose labor epidural analgesia is not initiated with a CSE technique.Our results suggest that the conversion of epidural analgesia to surgical anesthesia for category-1 CD in women with a functional labor epidural catheter is not associated with poorer neonatal outcomes compared to category-1 CD done under GA. This result further supports the early placement of epidural catheters for labor analgesia, especially in parturients at increased risk of emergent CD.


D1 inhibition, which seems to be related to the presynaptic inhibition, was recorded by measuring soleus Hoffmann’s reflex (H-reflex) with conditioned electric stimuli to the common peroneal nerve. Results: D1 inhibition was significantly enhanced immediately by the cycling intervention. The amplitude of the soleus Hmax was significantly depressed, and there was significant difference in Hmax/maximum M-wave. The MAV was increased due to inhibition of the stretch reflex. Conclusion: Leg cycling suppressed stretch reflex and H-reflex, and caused plasticity of inhibitory circuits in patients with CP with lower extremity spasticity. These findings strongly suggest that lower extremity spasticity can be improved by cycling movements.Objective: The purpose of this study was to investigate if leg cycling could reduce lower extremity spasticity in patients with cerebral palsy (CP). In addition, we investigated whether the intervention could cause changes in the modulation of presynaptic inhibition. Methods: This study was a quasi-experimental study, with pretest–posttest for 1 group. Participants in this experiment were eight adult patients with CP with lower extremity spasticity. Spasticity parameters assessed were the amplitude of soleus maximum Hoffmann’s reflex (H Cerebral palsy (CP) includes a group of permanent disorders of movement and postural development that cause activity limitations and can be attributed to non-progressive disturbances in the developing fetal or infant brain1). The motor disorders associated with CP are often accompanied by secondary musculoskeletal problems2). Spasticity is defined as a motor disorder characterized by velocity-dependent stretch reflexes with exaggerated tendon jerks, which is a symptom of upper motor neuron syndrome3). Spastic CP accounts for approximately 80%–90% of all patients with CP4); they suffer from symptoms such as muscle stiffness5), shortening, and increase risk for falls6). Moreover, the contracture responsible for stiffness causes further muscle overactivity, including spasticity7). Although spasticity has been attributed to lesions of the pyramidal tract, in animals, isolated lesions of the primary motor cortex (Broadman area 4) have been shown to decrease muscle tone and tendon reflexes instead of causing spasticity. Moreover, lesions of the premotor cortex and supplementary motor cortex (Broadman area 6) have been often shown to induce spasticity8,9). Even though pathophysiological mechanisms underlying spasticity in humans are not completely understood, the evidence suggests that an indirect descending pathway modulating the stretch reflex circuit from the motor cortex, such as the vestibulospinal tract, and presynaptic Ia inhibition is one of the spinal inhibitory interneuron systems involved in stretch reflex modulation10,11). D1 inhibition12), which seems to be related to the excitability of primary afferent depolarization interneurons mediating presynaptic inhibition of Ia terminals, deteriorates in CP13). In addition, several studies have reported that patients with stroke14) and multiple sclerosis15) show increased Hoffmann’s reflex (H-reflex) amplitude and impaired presynaptic Ia inhibition in the lower limbs in comparison with healthy controls.16), while no established evidence could be identified for therapeutic exercise. Nevertheless, we have experienced decreased muscle tone in patients with CP after cycling exercise in our daily clinical practice. Recent investigations have demonstrated that cycling movements induce a reduction in spasticity variables such as the H-reflex amplitude and Modified Ashworth Scale (MAS) scores17) and enhance inhibitory spinal pathways such as those mediating presynaptic inhibition in patients with diseases of the central nervous system other than CP18–21). In contrast, only a few studies have focused on the combination of electrophysiological measurements and use of this therapeutic intervention for spasticity in CP, and its effects on the modulation of spinal inhibitory interneuron systems in patients with spastic CP.Considering these findings, appropriate interventions for spasticity are extremely important, and its management is currently achieved through various therapeutic exercises, pharmacotherapy, and orthopedic surgery. In a recent systematic review on the evidence for interventions for CP, botulinum toxin, diazepam, intrathecal baclofen, and selective dorsal rhizotomy were recommended for reducing muscle spasticity22,23). The common core hypothesis suggests the existence of central pattern generators (CPGs) and regulatory systems that modulate reflex output within the spinal cord. The reason for focusing on D1 inhibition is that presynaptic inhibition may contribute to inputs from higher centers, CPGs, and even peripheral sensory receptors to modulate reflex outputs in the spinal cord.Rhythmic movements such as cycling and walking have different neural controls than normal voluntary movements24–26), and little evidence is available for the quantitative evaluation of spasticity. In this regard, the pendulum test27), which is performed by freely dropping the lower leg from knee extension in a relaxed state, has proven to be a reliable and objective method to assess various aspects of spasticity in CP28,29).Moreover, the spasticity in CP was often evaluated using the MAS according to published literature and in clinical practice, although some studies have questioned the reliability and validity of this evaluationThis study aimed to investigate if leg cycling could reduce lower extremity spasticity in patients with CP. In addition, we investigated whether the modulation of presynaptic inhibition  changed after this intervention.This study was a non-randomized, quasi-experimental study, with pretest–posttest for 1 group.30) (GMFCS). Six patients with CP were classified as level I and two were classified as level III. CP subtypes were also assigned by classifying four patients with spastic diplegia, one with hemiplegia, and three with mixed CP. While kinematic measurement was feasible for all participants, electrophysiological measurements showed that so while D1 inhibition was evaluable in seven patients, the maximum M wave (Mmax) in the soleus muscle was obtained from five participants because three of them could not tolerate the maximal supra-stimulus intensity to the nerve. The study protocol was approved by Sapporo Medical University Hospital Institutional Review Board (Permit Number: 282-32) and was conducted in accordance with the Declaration of Helsinki of 1975, as revised in 2013. Written informed consent was obtained from patients who participated in this study.Fourteen adult patients with CP were recruited from Sapporo Medical University Hospital. Six of them were unable to start the electrophysiological measurement protocol because of discomfort and excessive involuntary movements in response to the slight stimulus. The remaining eight patients  finally participated in this study. The functional level of participants was classified according to the Gross Motor Function Classification SystemInclusion criteria were: (1) adults with CP and lower extremity spasticity (quadriceps and soleus muscle MAS score ≥1), (2) ability to comply with simple verbal directions, (3) GMFCS levels I to III, (4) ability to pedal the ergometer, and (5) knee extension range of motion ≥–30°.Exclusion criteria were: (1) orthopedic surgery within six months preceding the experiment, (2) a history of botulinum injections or other medications used in the treatment of spasticity within three months preceding the experiment, and (3) difficulty in cycling without orthoses.Electrophysiological and kinematic measurements were obtained using the H-reflex method and pendulum test, respectively. Each measurement was obtained only for the more affected spastic lower extremity.The participants were required to take a break of 10 min before the experiment, subsequently seated comfortably in a wheelchair with the knees flexed approximately 70° and the ankle joint fixed in the mid position. Electromyographic (EMG) activities were recorded using bipolar surface electrodes  placed 2 cm apart over the muscle bellies of the soleus and tibialis anterior .12).The EMG signals were amplified and sampled at 2000 Hz, and band-pass filtered at 10–1000 Hz, capable of making measurements across a ±5 mV range (10 mV span) using an 16-bit A/D converter connected to a computer . Soleus H-reflexes were elicited by percutaneous stimulation  of the posterior tibial nerve in the popliteal fossa through the bipolar stimulating electrode. D1 inhibition was evoked by soleus H-reflex induced with an electrical stimulus applied to the common peroneal nerve (CPN)max), and both conditioned and control H-reflexes were recorded in 10 sweeps each (max to Mmax (Hmax/Mmax) was determined in the recruitment curve of each participant.The conditioning stimulus was generated at the dorsolateral aspect of the caput fibulae with a rectangular pulse of 1 ms duration and an intensity that was 1.2 fold the motor threshold, and it preceded the test stimulus with a 20-ms interstimulus interval (ISI). The control H-reflex intensity was adjusted at 50% of the ipsilateral soleus maximum H-reflex  was attached to the front of lower leg distal to the side tested, and the swinging motion with angular velocity of the rotation around the X axis was recorded and sampled at 250 Hz. The examiner passively lifted the lower limb to full extension and held the leg position until the state of complete relaxation for 5 s or more, as indicated by palpation and inspection of quadriceps contraction and spasm. Subsequently, the relaxed limb was released to swing freely and allowed to oscillate until it stopped at a resting position. The maximum angular velocity (MAV) during the first swing, which was obtained using the gyro sensor, was stored on a computer for subsequent offline analysis. As for data processing, zero point correction was performed on angular velocity from data in which the sensor was stationary for 3 s for each patient.All participants performed a 20-min exercise at a 20-W constant work rate on the ergometer. Adjustable foot straps and magic tape were used to hold the participants’ feet and prevent slipping. The saddle height was adjusted so that the knee flexion angle was approximately 20°–30° at the bottom dead center of the pedal. The participants were instructed to cycle at a comfortable rate with smooth movements without clonus and stretch reflexes. Therefore, we did not control the rotation speed for each participant, although made sure that it was not less than approximately 20 rpm. The positions of the surface electrodes and accelerometer were ensured to remain constant during the intervention.31) for only parametric tests . Statistical significance was determined by a p-value of 0.05.Statistical analyses were performed using the Statistical Package for the Social Sciences version 21 . The results were expressed as mean ± standard error of the mean. Differences between the measurements before and after the intervention were calculated with a paired two-tailed t-test. The effect size was calculated by Cohen’s dmax were significantly depressed , but no significant differences were observed in Mmax before and after the intervention , and so there was a significant reduction in Hmax/Mmax ratio . In addition, the MAV values significantly increased after the intervention  enhancement of D1 inhibition, (ii) suppression of H-reflex amplitude, and (iii) increase in MAV.10). An increase in D1 inhibition reflects the enhancement of presynaptic Ia inhibition, which is one of the inhibitory mechanisms in spinal neural circuits that are impaired in patients with spasticity13–15). In a previous study, the mean suppression of the H-reflex by D1 inhibition was 86.19% on the paralyzed side and 77.64% on the non- paralyzed side in stroke patients with spasticity (n = 29)13), 87.56% in children with CP (n = 21), and 69.56% (n = 21) in non-disabled children14). These findings are similar to the pre- and post-intervention suppression in the present study. Long-term cycling exercises in patients with spinocerebellar ataxia causing coordination defects have been shown to enhance D1 inhibition concurrently with improvements in ataxia score32). Thus, the enhancement of D1 inhibition may be related to agonist and antagonist control in the cycling task. Spastic CP is characterized by impairments in coordination and selective motor control in the lower extremities, particularly the distal joints1,33). Patients with spastic CP show high co-contraction ratios for the lower limb muscles during a single cycling session compared with healthy participants34). Whether an improvement in selective motor control of the lower extremities leads to enhanced D1 inhibition is a topic for future studies.The currently accepted hypothesis of spasticity focuses on inhibitory rather than excitatory mechanisms in the spinal circuit18,19,35,36). Furthermore, other studies have reported that the soleus H-reflex decreases after arm cycling exercise19). When it was conditioned with CPN stimulation during arm cycling37), a greater reduction than that in static control was observed. These findings support the role of excitability of presynaptic inhibition in the activation of supraspinal and propriospinal pathways and the CPGs in cyclic arm movement. Neural mechanisms such as the CPGs and peripheral feedback during rhythmic movement have specific modulatory effects on reflex inhibition38). The increased presynaptic inhibition in cycling probably contributed to the degradation of the input of Ia afferent fibers and suppressed the soleus H-reflex; additionally, D1 inhibition was enhanced in this study. Previous studies have shown that the soleus H-reflex is depressed by continuous passive and active cycling for at least 30 min. Since passive cycling involves almost no muscle contraction, afferent input from types III and IV afferent mechanoreceptors may be associated with presynaptic inhibition of Ia afferent terminals and contributes to reflex control35,39).Another possibility is that increases in presynaptic inhibition by rhythmic and reciprocal movements influence several spinal pathways and afferent fibers. Numerous studies have demonstrated that the H-reflex of the lower limb changes during and after lower extremity cycling exercise in a task-dependent manner40). Post-activation depression41), one of the presynaptic mechanisms underlying spasticity, is related to muscle spasticity13,14). However, a previous study has reported that inhibition of spasticity is not explained by its depression since cycling training decreased the soleus H-reflex and MAS score for calf muscle in contrast to the sustained post-activation depression observed in patients with multiple sclerosis19). Thus, H-reflex suppression after cycling exercise in patients with CP may have been caused by the modulation of presynaptic inhibition rather than post-activation depression.These findings suggest that downregulation of the H-reflex is less related to descending motor pathways such as corticospinal or corticobulbar tracts and is more associated with increased fusimotor drive, Ia activity, and primary afferent depolarization28). Another study found that among 20 children with CP, the mean MAV improved from 244°/s to 364°/s after selective dorsal rhizotomy to control spasticity29). In our study, the increase in the MAV was statistically significant, although it was not close to this value.The increased MAV indicated that the velocity-dependent stretch reflex in the quadriceps was inhibited by this intervention. The MAV (mean ± standard deviation) for children with (n = 10) and without (n = 10) CP in a previous report was 201.82 ± 67.96°/s and 292.51 ± 35.93°/s, respectively, suggesting that MAV is significantly lower in children with CP35,36). In the present study, the stretch reflex of the quadriceps was suppressed. While there are no studies assessing quadriceps stretch reflexes after pedaling, studies report decreased hamstring MAS42). Since pedaling generates muscle activity in the quadriceps, hamstrings, and lower leg in CP34), it is possible that the suppression of the reflex occurred in a task-dependent manner not only in the soleus muscle but also in the quadriceps.Suppression of the soleus H-reflex and enhancement of D1 inhibition may be the result of a specific effect of pedaling exercise. Previous studies have described task-dependent changes in the soleus H-reflex during and after pedaling in able-bodied people13) . In contrast, another study found that spastic stroke patients have impaired D1 inhibition compared to healthy participants14). Regarding the amount of H-reflex suppression, it is possible that stroke patients have a smaller degree of D1 inhibition of the damage despite the shorter time from onset than hemiplegic CP.The suppressed H-reflex in D1 inhibition could not be confirmed in the hemiplegic patient . Strongly impaired D1 inhibition in spastic hemiplegic CP has been previously reported, but the number of participants was small43,44). In the participants with hemiplegia in our study, D1 inhibition was not modified, whereas the reflex  was inhibited. Hence, the exaggerated reflex in hemiplegic CP must be affected by mechanisms other than presynaptic inhibition, but further data accumulation is necessary.We hypothesized that impaired H-reflex inhibition in hemiplegic patients with CP may be related to the timing of brain damage in infancy or fetal life because GABAergic neurons in the central nervous system have been shown to change from excitatory to inhibitory synapses during early neuronal development in animal testingThe clinical significance of these results is that the enhancement of the spinal inhibitory interneuron is a result of reciprocal movement. Moreover, the cycling task is easy to use in clinical practice and can be recommended as an exercise therapy. For future studies, the effect on motor functions such as walking speed and endurance, as well as the long-term effects and required duration of intervention need to be further examined. The intervention time in this study was within the same time frame as that of several previous studies, and similar results were obtained. However, there are reports of changes in spasticity scores in even shorter time periods.13,14,32). However, it is important to note that this value may vary among individuals12). Moreover, this experiment was performed with participants showing several types of CP , a wide range of ages, and a relatively small sample size, which could limit the interpretation of the results. Nevertheless, we anticipate that cycling movements will become an effective intervention for the treatment of spasticity in patients with CP.The present study has some limitations. The ISI required to obtain maximum suppression of the H-reflex in D1 inhibition is approximately 20 ms, and many studies have used 20 or 21 msThe findings of the present study suggest that cycling intervention in adults with CP can induce suppression of H-reflex and stretch reflex in the leg, and activation of presynaptic inhibitory networks within the spinal cord. Therefore, leg cycling may reduce spasticity in the lower extremities of patients with CP.We are grateful to Dr. Megumi Toki, Assistant Professor of Sapporo Medical University, for her important contributions to the experiments. This study was supported by the Japanese Physical Therapy Association (JPTA H28-B39).The authors have no conflicts of interest in relation to this manuscript.


Various studies have shown that medical professionals are prone to follow the incorrect suggestions offered by algorithms, especially when they have limited inputs to interrogate and interpret such suggestions and when they have an attitude of relying on them. We examine the effect of correct and incorrect algorithmic suggestions on the diagnosis performance of radiologists when (1) they have no, partial, and extensive informational inputs for explaining the suggestions (study 1) and (2) they are primed to hold a positive, negative, ambivalent, or neutral attitude towards AI (study 2). Our analysis of 2760 decisions made by 92 radiologists conducting 15 mammography examinations shows that radiologists’ diagnoses follow both incorrect and correct suggestions, despite variations in the explainability inputs and attitudinal priming interventions. We identify and explain various pathways through which radiologists navigate through the decision process and arrive at correct or incorrect decisions. Overall, the findings of both studies show the limited effect of using explainability inputs and attitudinal priming for overcoming the influence of (incorrect) algorithmic suggestions. Especially in the medical context, this has major implications for the development and deployment of these algorithms since medical decisions are high stakes and carry strict legal liabilities3.The introduction of data-driven algorithms in the domain of medical imaging is one of the leading areas of technological development. A distinguishing characteristic of new algorithms is their black-box character: the complexity of understanding the relations between inputs and outputs7. For instance, in examining mammograms, radiologists who were assisted by CAD were more likely to miss pathological findings when such indications were also missed in the CAD results4. The results of another study demonstrated that CAD improved sensitivity for relatively easy-to-detect cancers, but decreased sensitivity for more difficult cases5. This decrease in sensitivity for challenging cases was attributed to radiologists' increased reliance on CAD when they were uncertain of their own decisions8.Several studies have shown that  professionals are prone to be impacted by the suggestions and inputs provided by various forms of algorithmic tools such as computer-aided detection (CAD) and different forms of artificial intelligence (AI). CAD, a precursor to more advanced AI-based systems, has been extensively studied for its effects on diagnostic accuracy and the potential for misuse. Despite their potential, various studies have found that CAD tools can decrease the specificity and sensitivity of radiologists’ decisions10. Similarly, researchers found that employing CAD as a concurrent reader significantly reduced the reading time compared to radiologists working without CAD assistance11. Consequently, this reduction in thoroughness was associated with a diminished sensitivity in comparison to readings conducted without CAD support11.Furthermore, studies have shown that the potential for CAD misuse is heightened when used as a concurrent reader (immediate availability of CAD output) rather than a second reader. For instance, researchers discovered that a poorly performing CAD system used concurrently significantly diminished radiologists' diagnostic performance in mammogram reading13. This challenge is exacerbated in practice when medical professionals are presented with the “outcome suggestions” made by the algorithms , with limited informational inputs to interrogate and interpret how such suggestions are derived from the specific inputs.Despite ample research on designing data-driven algorithms, there is relatively limited research on the interactions that emerge when medical professionals interact with the results offered by these algorithms13. However, in practice, forms of limited reflective engagement with AI suggestions have been documented by various studies. Here, two mechanisms of cognitive bias are likely to occur: (1) over-reliance, agreement with AI suggestions with no or limited reflective engagement and (2) under-reliance, disagreement with AI suggestions with no or limited reflective engagement14.Ideally, professionals engage in “reflective practices” through which they directly inspect medical images and informational inputs, and consider various medical scenarios, compared to the situation in which they do not have such suggestions15, the preference of seeking confirmation over disconfirmation, the trust in the algorithm in general or in the specific outcomes16, and limited inputs for and capabilities of interrogating the AI and its suggestions17. These are because human actors have limited capacity to process information19, hence they try to ignore some informational inputs and use “mental shortcuts” to perform cognitive tasks20.Over-reliance on algorithmic suggestions is hypothesized to be rooted in the human tendency to minimize the cognitive efforts for performing a (complex) task21. For instance, it is found that both low and high levels of expertise can result in algorithmic aversion, for lack of capability to appropriately understand the correct suggestions and for the strong sense of accountability for their own decisions against AI suggestions, respectively22.Under-reliance is hypothesized by the negative attitudes about AI, the strong sense of status and autonomy in making decisions by professionals, and the lack of inputs for and capabilities of interpreting the AI suggestions23, the paradox of their occurrence has been one of the still unsolved dilemmas24. Research so far offers inconsistent insights regarding which factors can diminish these two biases. Various studies have examined the (co)relation between some factors such as the accuracy of AI suggestions12, the level of expertise of professionals25, and the possibilities for engaging in some additional reflection13 and the forms of reactions to AI suggestions in terms of accepting, rejecting, or ignoring12.Where over-reliance and under-reliance pose antithetical examples of inappropriate reliance26, current studies do not offer a consistent pattern regarding when each dynamic is more likely to happen and how they can be diminished. Current studies propose two broad categories of factors which are expected to alleviate these two biases: (1) providing explainability inputs next to AI suggestions and (2) developing a critical or balanced attitude about AI and its suggestions.Although the overall pattern is that professionals are prone to both over-reliance and under-reliance biases28. In the case of AI-based algorithms applied to medical imaging, two types of explainability inputs are relevant: morphological and numerical inputs.Theories have hypothesized different impacts of “explainability” inputs in moderating the effect of algorithmic suggestions on human decisions. In particular, information processing and rational decision-making theories suggest that providing information which enables users to trace algorithmic suggestions to the specific attributes of the inputs can support them in their analytical reflection. This allows for the independent and rational analysis of the accuracy of such suggestions. Explainable AI methods can enable users to understand and interact with the explanatory inputs8.Morphological inputs are mentally close to the images and anatomical regions that medical professionals are used to seeing and understanding. These inputs highlight the areas of the medical image on which the algorithm bases its findings. Such highlighting is often presented as heatmaps, also known as “saliency maps”. These heatmaps draw attention towards salient regions of the images, supporting the attentional focus of users on potentially relevant areas. However, when the algorithmic suggestions are incorrect, the heatmaps may misguide users’ attention towards areas that the algorithm has mistakenly considered relevant, increasing the risk of overlooking relevant areas30.Numerical inputs include quantitative attributes of a medical case, including the general image characteristics such as density, uniformity, margin, and shape of the medical image, as well as the clinical attributes such as age and historical and genetic characteristics, which are potentially relevant for the outcome decision. The interpretation of these attributes is often based on their relative contribution to the algorithmic suggestion for each case. Presenting these attributes and their relative contribution to the AI outcome can trigger a deeper understanding of how a specific suggestion has been made by the algorithm31. In the second mechanism, the very presence and complexity of the explainability inputs can create the impression of soundness of AI and its suggestion, hence causing users to follow the AI suggestions without examining their veracity32.However, providing these inputs can evoke other attentional and cognitive mechanisms that can lead users to instead minimally engage in critical examination. Two mechanisms can play a role here: (1) the tendency to avoid information overload, and (2) developing confidence based on the mere presence and apparent sophistication of presented inputs. In the first mechanism, when users perform complex tasks that place pressure on cognitive resources , they may use the explainability inputs selectively and instrumentally to justify their own intended decisions (being either agreeing or disagreeing with AI suggestion). This results in a more heuristic-based usage of AI suggestions in order to compensate for the increased cognitive effort instead of systematically and critically examining the AI suggestion33. Whether professionals hold a positive, negative, or ambivalent attitude towards either algorithms in general or the specific algorithm and suggestion, is considered to impact both their analytical engagement and their reliance on the AI suggestions23. Although a minimum level of negative attitude can trigger them to critically examine AI suggestions, carrying a strong negative attitude about AI can lead to disregarding the value of considering AI suggestions entirely and hence causing under-reliance21. In extreme cases, this can lead to opposition towards AI, meaning that the professionals’ attempt is focused on finding ways to disprove and counter its suggestions, regardless of their validity.The second category of factors moderating the effect of AI suggestions on human decisions are attitudes towards AIOn the other hand, a positive attitude towards AI and its suggestions can trigger mental and attitudinal openness to consider such suggestions and rely on them when users deem these suggestions correct. At the same time, developing a (strong) positive attitude towards AI may cause overconfidence in its suggestions and prevent users from holding a critical, independent analytical stance, resulting in over-reliance on AI suggestions. Therefore, some scholars have hypothesized that holding an ambivalent attitude, namely both positive and negative, can overcome both biases, if professionals use the explainability inputs for a critical examination of AI suggestions.How is the effect of AI suggestion on professionals’ decisions moderated by providing different explainability inputs and attitudinal priming?As Fig. various dynamics may occur, of which each can potentially cause different decisional pathways in terms of how experts engage with the task and make their decisions. This assumption is more consistent with the recent findings indicating the heterogeneity of ways in which  professionals react to the same inputs, depending on how they engage with and navigate through the diagnosis process25.To answer this question, we assume that when  experts are provided with AI suggestions, Departing from this assumption, we argue that it is not enough that we only examine whether a specific input  leads to a specific outcome . Rather, we need to explore the various ways in which human actors interact with AI suggestions (and other inputs) and eventually make their own decision. In this view, human actors are considered as actors who have the agency to actively choose different ways of acting on the inputs and render their decisions.Taking an exploratory approach, we examine this question in the context of conducting mammography examinations by radiologists. By analyzing 2760 decisions made by 92 radiologists, examining 15 pairs of mammography images, we show that the effect of AI suggestions on the diagnosis decisions are strong and consistent, regardless of explanation inputs and attitudinal priming. We identify distinct pathways that lead radiologists to over-diagnose, under-diagnose, and correctly diagnose. As we will show, the effects of explainability inputs and attitudinal priming are limited to some specific pathways, and cannot overcome the influence of AI suggestions.34. We deployed a custom-developed online environment which allowed us to design the experiments and collect the required data. Both studies followed a between-subject design between different treatment groups. The different treatment groups are exposed either to different types of explainability inputs (Study 1) or were primed differently in relation to the capability of AI (Study 2). The participants are randomly assigned to the different groups.The underlying research is split in two quasi-experimental studies. This approach was chosen because a lab experiment can be conducted under highly controlled conditions where accurate measurements of causal relationships are possible in an artificial environmentIn study 1, we experimented with different types of “explanation inputs”, namely (1) a heatmap and (2) numerical attributes of the cases. We treated both explainability methods as a gradual extension of information that explains the AI suggestion.Explainability-control: no access to any explainability inputs (only supported by an AI suggestion)Explainability-partial: only able to access the heatmap onlyExplainability-full: access to both heatmap and case attributes.We designed three 3 groups:Priming-control: received no primingPriming-positive: shown a video instruction on the effectiveness and potentials of AI tools for making medical diagnosisPriming-negative: shown a video instruction on the pitfalls and limitations of AI tools for making medical diagnosisPriming-ambivalence: show a video on both positive  and negative  aspects of AI and its outcomes.In study 2, we kept all the explainability inputs, but we experimented with different modes of “attitudinal priming”. We designed 4 groups:To design the attitudinal priming in a way that represents the real-life of framing AI to participants, we edited the videos to contain positive and negative facts about AI in a medical context, presented by authorities from the field of medical AI and imaging informatics as reported in Appendix Cases35. These cases were selected by a panel of 3 experienced radiologists to be representative of the common cases in clinical conditions. In consultation with several radiologists, we aimed for 15 cases to provide a feasible set of tasks to be examined continuously, without requiring any break time or reducing the focus and attention due to the fatigue.All participants were presented with the same sequence of 15 mammograms in the craniocaudal view on the left side of the screen and mediolateral oblique view on the right side of the screen. In both views, the left and right sides of the breast are shown , with minor (1 point error) and major (2 points error) deviations from the ground truth, equally balanced in terms of “over'' and “under” diagnosis. This way, out of 30 suggestions (2 suggestions per each mammogram), we provided 8 incorrect suggestions (27%) to represent enough incorrect suggestions but not too many to create a mistrust in the suggestions entirely. Because of the clinical interchangeability of BI-RADS score 1 and BI-RADS score 2, the choice was made to solely include cases with BI-RADS score 2 to prevent ambiguity between participant’s answers. The following Figure shows the order of the cases and the AI suggestions. We also designed an equal number of “over-diagnosis” vs. “under-diagnosis” AI suggestions (4 each) to be able to control for the effect of this factor on the outcome decisions of radiologists. We intentionally aimed for more small errors (6) than large errors (2) in terms of AI suggestions to represent realistic scenarios where the chance of offering large errors is smaller than the chance of committing small errors. Having many big errors could create major mistrust in the AI suggestions and potentially ignoring them.AI suggestions were not visible to the participants, unless they clicked on the “Show AI suggestion” button see Fig. .Figure 2Classification inputParticipants were presented with the BI-RADS classifications scale (only 1–5) for each breast and were asked to provide their BI-RADS decision on a breast-side level. BI-RADS category 0 is left out because the experiment only allows complete assessment; BI-RADS category 6 is left out because this category requires a biopsy of suspicious tissue, however, this is not related to this research project which just covers image recognition with the help of AI. We traced how fast they gave their first classification and how frequently they changed their decision before submitting each case.Truth was determined from the original clinical reports  and a third confirming reading by an independent experienced reader. Since our study focuses only on BI-RADS assessment , we did not distinguish between benign vs. malignant. The measurable lesions had an average size of 15.75 mm  to 3 , whereby the bars in the bar charts represent the ratio between these ratings. Values from -3 till -1 are represented as bars to the left (indication for benign finding) and values from 1 to 3 are represented as bars to the right . The case attributes could be accessed by hovering the mouse over the information field above the "Submit answer & continue" button . We asked the participants about their experience of performing mammography  as well as their experience in terms of using AI/CAD tools . As Fig. The study obtained the approval of the ethical committee of the department of the principal investigators by carefully examining the potential ethical and legal consequences. The research was conducted outside of the clinical context and did not impact any clinical decisions on specific patients. Informed consent was obtained from all participants in the research. All participants participated in the research on a volunteer basis and they were informed about the purpose of the study and what they are going to do in advance. In addition, they all had the chance to actively “opt-in” for participating with the condition that their data would be used for the research. All the obtained data were immediately anonymized at the start of the analysis. All methods of collecting and analyzing data and conducting research were performed in accordance with the relevant guidelines and regulations.40. This way, we avoided potential fishing of  significant patterns which may not be meaningful. Hence, we constantly iterated between the empirical patterns and the specific cases and used the granular data on the patterns of actions performed on the cases to (1) identify the heterogeneity of the patterns and (2) examine various  explanations40.The operationalization of concepts and analyses were at the different levels of analysis, namely (1) mammogram level (both entire mammogram and each breast-side), (2) participant level, and (3) study group level. For each participants, we obtained 2 decisions on each mammogram (one BI-RADS scoring per each breast-side), resulting in (2*15) 30 decisions at the breast-side level . When we aggregated some factors as the level of mammograms , we reported this data and analysis at the mammogram level (hence having 92*15 = 1380 data points). Given the exploratory character of the study, we adhered to the principle of capturing and describing heterogeneity in the phenomenon and did not oversimplify the patterns based on single-model analysisWe operationalized our outcome variables based on the final decision performance of the human actor compared to the ground truth of the underlying BI-RADS category for the mammogram as well as the given AI suggestion  the level and recency of experience in performing BI-RADS classification and (2) the level and recency of experience with using CAD/AI tools. The first control variable that was deemed as closely related to the engagement with the tasks and interacting with AI suggestions was (1) recent experience of classifying mammograms. It might be that radiologists who just recently read an increased number of mammograms will be able to identify abnormalities in a mammogram more accurately and quickly in a substandard artificial clinical setting without additional cognitive exertion owing to their routine.The second control variable that was deemed as relevant was the experience of using CAD/AI tools. This variable was also added in consultation with one of the involved senior radiologists, who noted that a lot of radiologists had bad experiences with CAD systems in the past and could therefore be negatively preoccupied with AI. This could have caused greater distrust towards the underlying pseudo-AI among radiologists who already had experience with CAD and they may have had a negative attitude towards AI upfront.In addition, we explored the “analytical pathway” through which participants navigated through the tasks and interacted with the various informational elements. This concept emerged as an important way of explaining “how” participants engaged with the tasks. In particular, we analyzed the sequence of viewing “Heatmap”, consulting “AI suggestion”, and making “Decision” in performing each task. We also paid attention to the timing of such sequences in order to understand different pathways and analytical journeys of participants. By crossing these analytical pathways with different groups and outcomes, we tried to make nuance interpretations of the theoretical relations.41. There is no significant difference between the various groups in terms of the time of performing tasks. Nevertheless, the accuracy of decisions is significantly higher for cases in which radiologists spent more than the average time, compared with those conducted less than the average time . During the experiment, the average time spent on tasks slightly reduces .On average, participants spent 1.3 min per mammogram, with a standard deviation of 71 s, which is comparable with the clinical reading time2  = 1.8, p > 0.605).Participants consulted AI suggestions in 84% of the tasks, with a tendency to do so in the second half of the time of conducting the examinations. Accessing AI suggestion was not correlated with lesion type , which showed that this informational element was not consulted at the level that can be influencing their decisions. There is a strong association between the number of times that the case attributes are visited and the accuracy of the diagnosis.We examined the sequence of consulting AI suggestions and heatmap, before making the final diagnosis decision. The most frequent pattern was related to (52%) first consulting the heatmap and then AI suggestion, followed by only consulting AI suggestions (23%), first consulting AI and then examining heatmap (9%), only consulting heatmap (8%), and skipping both AI suggestions and heatmaps (8%).At the most granular level, each task involved making two decisions: scoring left and right beast sides. On 2760 decisions, radiologists made 64% correct decisions, which is lower than the overall accuracy of AI suggestions (73%). Radiologists have a tendency towards over-diagnosis (20%) than under-diagnosis (16%). When AI suggestions are not consulted, the share of correct decisions is 61% and when AI is consulted, it is 65%. As Fig. The type of decision made by radiologists has an agreement of 73%, 79%, and 58% with the type of AI suggestions for “under-diagnosis”, “correct”, and “over-diagnosis”  across the 15 mammograms. As Fig. This pattern is more vivid when we look at each breast-side see Fig. . The errThere is no significant difference across “explainability-control”, “explainability-partial”, and “explainability-full” groups in terms of the diagnosis errors of radiologists.There is also no significant difference across the four groups in study 2, where they received different types of attitudinal priming. In addition, there is no significant difference between the average of human error across the different levels of experience with AI/CAD Table  and the Noticing the strong association between AI suggestions and radiologists’ decisions, it is helpful to explore the pathways that are associated with correct, over- and under-diagnosis decisions. For each of these decision types, there are various pathways depending on the type of AI suggestion and whether radiologists consulted AI suggestions  can be mapped to 3 pathways.and radiologists consult AI suggestions (76%). This pathway happens across all groups, though is most popular in the “Priming-positive” group. These cases are relatively more popular when the true scores are “2” (34%) or “5” (37%) than being in the middle of the range, “3” (11%) or “4” (18%). There is no pattern regarding the average time spent on the task, the level and recency of experience, or experience with using CAD/AI tools.The most dominant pathway (Corr-1) is when the AI suggestions are correct The second pathway (Corr-2) is when AI offers an incorrect suggestion and radiologists consult such suggestion, yet they make a correct decision (13%). This pathway is observable in all groups and has the highest relative popularity among least (less than 5 examinations per week) and most (more than 50 examinations per week) experienced radiologists. This pathway never happens when the true score is “5”. Overall, the average time spent on these tasks (89 s) is higher than the total average (79 s).not consult AI suggestions (15%). These cases were present in all groups, with a lower relative frequency in Priming-positive (1%) and Priming-negative (6%) groups, and higher relative frequency in Explainability-control (27%) and Priming-ambivalence (25%) groups. These cases are more popular when radiologists have a moderate level of experience  or had a recent experience of conducting such examinations . This pathway is mainly visible in clearly normal  or clearly abnormal  cases. The average time spent on these tasks (63 s) is lower than the overall average (79 s).The third pathway of making correct diagnosis (Corr-3) is when radiologists do From all the cases that radiologists make an over-diagnosis of 1 or 2 scores, in one third of them they consult AI suggestions, which also indicates an over-diagnosis (Over-1). This happens in all groups and there is no pattern in terms of the experience with AI/CAD nor the frequency or recency of performing such examinations. However, this pathway is only observed in cases with a true score of “2” (82%) or “3” (18%) and overall, the average time spent (113 s) is higher than the overall average (79 s).The most common pathway (Over-2) for making an over-diagnosis decision is when AI offers a correct suggestion but radiologists consult AI suggestion and deviate from it towards a higher score (46%). In 87% of these cases, the true score is “2”. There is no other pattern regarding the experiences with AI/CAD or conducting examinations. The average time spent on these tasks (95 s) is higher than the overall average (79 s).In 5% of cases, AI suggestions is an under-diagnosis, yet radiologists go in the opposite direction and make an over-diagnosis (Over-3). These cases are present only in mammograms “6” and “13″ where AI deems both breast-sides as normal, although the cases had some issues. These cases were present in all groups, except Priming-control and Priming-positive”, and did not happen when radiologists had a high level of experience (more than 50 per week). In all these cases, the participants spent less than average time on performing the tasks (59 s on average).In 17% of the cases, radiologists did not consult AI suggestions and made an over-diagnosis of 1 or 2 scores (Over-4). These cases were mostly popular in Priming-ambivalence and Explainability-partial groups, and never happened when radiologists were negatively primed. These cases never happened when radiologists had a high level of experience (more than 50 per week). Almost all these cases were related to situations where the true score was “2” (80%) or “3” (19.5%). On average, these cases were conducted in a shorter time than the average time (59 s).Finally, in 7% of the cases, radiologists made an over-diagnosis of 3 points above the true score (Over-5). These cases only happen when radiologists had no experience of conducting mammography in the last 6 months, and on average, the time spent on these tasks is above the average (95 s).In very few cases (5 out of 2760) radiologists made an under-diagnosis of 3 points below the true scores (Under-5). For the rest, the most popular under-diagnosis pathway  is when AI offers an under-diagnosis and radiologists consult such a suggestion. These cases are rather evenly popular in all groups, across different levels of experience (both frequency and recency). On average, these cases were examined in a comparable time as the overall average (75 s).In one-fourth of the under-diagnosis decisions, radiologists consulted the correct AI suggestions, yet they deviated towards lower scores (Under-2). These decisions were made when radiologists were experienced  and they were not positively primed (96%). This deviation is more likely to happen in the cases with a high level of true score (“5” or “4”). On average, these cases were examined in a comparable time as the overall average (82 s).A small number of under-diagnosis cases (4%) relate to conditions that AI is offering an over-diagnosis suggestion, yet radiologists take the opposite direction and make an under-diagnosis decision (Under-3). This situation happened only in the mammograms #10 and never happened when radiologists had an intensive activity of doing mammography (more than 50 per week). On average, these cases were conducted in a longer time (98 s) than the average.Finally, in 18% of the under-diagnosis cases, radiologists did not consult AI suggestions. There is no specific pattern in terms of the groups and task-related factors (Under-4). In 74% of these cases, the participants did not have any experience with AI/CAD before. On average, these tasks were conducted in 67 s.26 especially when such suggestions are incorrect and limit the focus and analytical engagement of human actors.Overall, our findings show that radiologists’ decisions follow AI suggestions in terms of the error size  and type . The chance of making correct diagnoses is 78% when consulting correct suggestions and the chance of making incorrect diagnoses is 72% when consulting incorrect decisions. This pattern is visible across all the 15 tasks and especially the specific breast-sides on which the (incorrect) suggestions are offered. This confirms prior research showing that the presence of algorithmic suggestions can reduce the accuracy of diagnostic decisionsOverall, the diagnostic error was not significantly different across experimental groups, participants with different levels of experience in performing the task (both frequency and recency) and experience with CAD/AI tools.42. In fact, when these inputs are close to the mental models of experts, they are consulted and used as heuristics for reflecting on the case and the algorithmic suggestion. This confirms the explanation that professional experts tend to use the forms of explanation that are closer to their mindsets and require less cognitive effort for understanding them31. This can also be because heatmaps offer a form of cognitive short-cut for radiologists to focus on specific morphological areas of the image and to ignore the other parts, which might be a reason for the tendency towards over-diagnosis than under-diagnosis8. We also did not see any specific pattern showing that tasks performed with consulting AI suggestions took a shorter time than those performed without consulting AI suggestions.Regarding the explainability inputs (Study 1), we see that participants sparingly used the numerical inputs (case attributions), yet they consulted the heatmap frequently and did so often before they consulted the AI suggestion. Our findings show that explainability inputs are not entirely overlookedAs for attitudinal priming (Study 2), we do not see a clear effect on the diagnostic decisions. The effect of these interventions appear in some specific pathways. This can be partly because participants had an overall limited experience with CAD/AI tools, but were generally experienced in performing the tasks. Therefore, triggering them to be positive and/or negative towards such algorithmic tools in general was not comparable with the effect of the factual inputs that they interacted with .The various pathways leading to correct, over- and under-diagnosis show that there is no single pattern that can be generalized to all situations. To arrive at correct diagnoses, radiologists may confirm correct AI suggestions , they also may manage to appropriately disagree with incorrect suggestions  especially when they spend extensive time. They can also make correct decisions without consulting AI suggestions  when the cases are obviously normal or obviously pathological. Even with correct AI suggestions, radiologists may spend above average time on the cases to find some issues (over-diagnose), when they are not primed to be positive about these suggestions.Finally, to arrive at under-diagnosis decisions, the most common path is consulting an under-diagnosis AI suggestion . However, radiologists who have frequent experience and who are not positively primed may spend extensive time to disprove correct suggestions, particularly when these suggestions concern high BI-RADS scores . Finally, spending limited time on a task while ignoring AI suggestions and spending a relatively short amount of time examining the heatmap, together lead to missing pathologies in the cases and lead to under-diagnosing decisions .Several conditions that limit the generalizability of our findings and hence call for future studies.43. These participants have limited experience in terms of how to react to AI suggestions and interrogate them. Hence, it is important to investigate how the experience of working with these algorithms impacts the way radiologists make decisions.Generally experienced participants, but limitedly experienced in terms of using CAD/AI tools are representative of the majority of the current radiologists36. Although many current AI applications focus on these forms of tasks44, there are other forms of algorithms which offer inputs for making less structured decisions, such as segmenting certain lesions and offering measurements that are not the final decision, but rather are part of the informational inputs for making a medical decision. This different role of AI in the medical decision-making process can potentially trigger distinct ways of interacting with them and using them.Having 15 cases, with rather uneven distribution of error types, did not permit for systematic comparisons between different case types  or different types of AI suggestions . Also, uneven distribution of correct and incorrect cases limited a systematic analysis of their effects within (sub)groups. “BI-RADS classification” represents a structured decision with objective decision categoriesOur experiment was conducted outside of a clinical setting because the experiment application was made available online and allowed participants to participate at any time and from any location with their own private equipment. The monitors that are typically used in a clinical setting to analyze mammograms are technically superior to traditional monitors. This may have underrepresented fine abnormalities such as microcalcifications. We attempted to mitigate this limitation by adding a zooming option, but the "low-resolution" restriction remained. In addition, the online experiment limited the possibility of controlling for the environmental distractions and ensuring a comparable background light as is provided in the clinical reading rooms.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.


Venous thromboembolism (VTE) in pregnancy is a major cause of maternal morbidity and death. The use of low-molecular-weight heparin (LMWH), despite being the standard of care to prevent VTE, comes with some challenges. Shared decision-making (SDM) interventions are recommended to support patients and clinicians in making preference-sensitive decisions. The quality of the SDM process has been widely assessed with the decisional conflict scale (DCS). Our aim is to report participants’ perspectives of each of the components of an SDM intervention (DASH-TOP) in relation to the different subscales of the DCS.Design: A convergent, parallel, mixed-methods design.Participants: The sample consisted of 22 health care professionals, students of an Applied Clinical Research in Health Sciences (ICACS) master program.Intervention: We randomly divided the participants in three groups: Group 1 received one component (evidence -based information), Group 2 received two components , and Group 3 received all three components  of the SDM intervention.Analysis: For the quantitative strand, we used a non-parametric test to analyze the differences in the DCS subscales between the three groups. For the qualitative strand, we conducted a content analysis using the decisional conflict domains to deductively categorize the responses.Groups that received more intervention components experienced less conflict and better decision-making quality, although the differences between groups were not statistically significant. The decision analysis recommendation improved the efficacy with the decision-making process, however there are some challenges when implementing it in clinical practice. The uncertainty subscale showed a high decisional conflict for all three groups; contributing factors included low certainty of the evidence-based information provided and a perceived small effect of the drug to reduce the risk of a VTE event.The DASH-TOP intervention reduced decisional conflict in the decision -making process, with decision analysis being the most effective component to improve the quality of the decision. There is a need for more implementation research to improve the delivery of SDM interventions in the clinical encounter.The online version contains supplementary material available at 10.1186/s12911-023-02349-3. Venous thromboembolism (VTE) in pregnancy causes approximately 1.5 to 2% of maternal deaths during pregnancy and the postpartum period, and is a major cause of maternal morbidity , 4. PropShared decision-making (SDM) is the gold-standard approach when dealing with preference sensitive decisions , 10. Theexpected value ) considering individualized data on the patient’s risks and preferences toward different health states [The international collaborative group DASH-TOP  , is deveh states . These ch states , 14, 15.h states . The DASh states , with woh states , there ah states . The Dech states , 15. Theh states , 20 thath states . The DCSh states , 21. Howh states , and usih states .Our research question was: What are participants’ perspectives of each of the components of the DASH-TOP intervention in relation to the different subscales of the DCS?We adopted a convergent, parallel, mixed-methods design , 25, incWe recruited postgraduate health care professionals who were enrolled in the Applied Clinical Research in Health Sciences (ICACS) master program at Universidad Autónoma de Barcelona during the 2020–2021 and 2021–2022 academic years. Participants were simultaneously working as health care professionals. Subjects were informed about the study and gave written consent to participate. The study was approved by the ethical committee board of Hospital de la Santa Creu i Sant Pau (IIBSP-TDC-2018-02) in accordance with the Declaration of Helsinki.Data were collected using convenience sampling. We recruited participants, collected the data and analyzed it for each academic year, continuing recruitment the following year until saturation  was reached , 29. AftThe intervention was delivered as part of the master program’s module on “Values and Preferences” taught by two of the authors . It was conducted using a real-time video conferencing platform (Zoom) due to the COVID-19 pandemic. All participants were presented with a case study  into three different virtual groups. We followed an incremental design with varying exposure to the different components: group 1 was exposed to the first component of the SDM intervention, group 2 to two components, and group 3 to all three components. Figure The virtual room was divided into three rooms to ensure blinding of participants and to avoid interference between groups. Researchers delivered each component independently to each group as follows:I)Group 1: This group received only the evidence-based information component of the intervention. Participants were presented with scenarios for four clinical situations : suffering DVT in pregnancy, suffering PE in pregnancy, suffering major obstetric bleeding (MOB), and the inconvenience of using LMWH in pregnancy; all of which had been previously validated , 7. ThisII)Group 2: This group first received the evidence-based information, followed by two value elicitation exercises: 1) a ranking exercise in which participants were asked to rank the four health states, from most to least preferred; and 2) a visual analogue scale (VAS) in which participants placed each health state along a “feeling thermometer” that represents the importance of suffering each health state for them, on a scale of 0 (death) to 100 (well in pregnancy). The percentage value placed on the feeling thermometer matches the value filled on the table.III)Group 3: This group received all three components of the intervention: evidence-based information, value elicitation exercises and a decision analysis recommendation. Their value ratings obtained from the VAS exercise were inputted into a decision analytic model. The decision analytic model is a Markov state transition model developed for the DASH-TOP intervention , 12, 30 Participants were asked how long it took them to complete the intervention. After completing the intervention, participants were asked to complete the DCS and then respond to an open-ended questionnaire regarding their perceptions of the different components and the decision-making process from their perspective as health professionals visiting a woman with this clinical situation Fig. .We used the DCS instrument , 20 to eFor the qualitative data, all participants were asked to respond to six open-ended questions to assess the factors involved in the decision-making process, whether the information was appropriate, and how they balanced harms and benefits of the treatment alternatives. Additionally, groups 2 and 3 were asked to describe their experience and perceptions of the value elicitation exercises, and group 3 was also asked to describe the usefulness of the decision analysis to make a decision and their confidence with the decision-making process. In Supplementary material SD) for the total DCS and each subscale. No missing values were found. Given the small sample size, and after testing the basic assumptions , Kruskal–Wallis H non-parametric test was used to analyze the differences in DCS between the three groups. SPSS 26 for Windows was used for all quantitative analyses.We conducted descriptive analyses to report the mean and standard deviation  independently coded the transcripts by conducting a content analysis using the decisional conflict subscales to deductively categorize the responses. For example, we collected responses related to how clear it was to decide the benefits and drawbacks of taking medication, and matched it with the values clarity subscale.Quantitative and qualitative results were merged; this process can be found in Supplementary material n = 7); G2 Value elicitation exercises component (n = 7); and G3 Decision analysis (n = 8). Participants in G1 spent on average 18 min to complete the intervention; for G2, it was 26 min; and for G3, it was 33 min.We approached 44 health master students, 22 (50%) of whom agreed to participate (16 from the first academic year and six from the second). Reasons for not participating were not collected. The majority of participants were women . All students were working as health professionals of the following disciplines: eight (36%) nurses; five (23%) physicians ; four (18%) physical or occupational therapists; two psychologists (9%); two dentists (9%); and one (5%) microbiologist. Supplementary material Data collection and analysis is accessible in Supplementary material H(2) = .06; p = .97).Overall, decisional conflict scores were low  for all three groups. For the total DCS, Group 1, who received only the evidence-based information intervention, showed higher conflict score , and two (28.6%) of the participants were unsure of what their final decision would be after completing the intervention. Those that also completed the value elicitation exercises (Group 2) presented a lower conflict . Only one participant (14.3%) felt unsure of what their final decision would be after completing the intervention. Furthermore, those that also received the decisional analysis model result (Group 3) had the lowest level of conflict ; no conflict), and none of the participants felt unsure of what their final decision would be after completing the intervention. However, despite these trends, no significant differences between the intervention groups were observed (Table H(2) = .22; p = .89). The results follow the same pattern as the total decisional conflict score showing higher conflict for Group 1 than Group 2, with Group 3 showing the least conflict. For all groups, there was concordance between the quantitative and qualitative results.This subscale informs about the presence of conflict regarding the knowledge participants have around the benefits and risks of the options. The mean score for this subscale showed no conflict  and there were no significant differences between groups , the graphical representation of the risks and benefits of taking LMWH (43%), and their previous experience (29%). Some participants (29%) showed some conflict (> 25-<37.5) because they didn’t know if other options were available . In Group 2, all participants (100%) were aware of the options, and exclusively referred to the graphical representations to inform about the benefits and risks of each option. There was one outlier that showed a higher conflict (> 37.5) in comparison with the rest of the group; this participant reported not having enough information about the benefits of taking LMWH. Group 3 felt they knew the risks and benefits of each option due to: the information provided by the health states (50%); the graphical representations (38%), and previous experience (25%).H(2) = .24; p = .89). Group 1 and Group 2 showed the same level of conflict, and the quantitative and qualitative results were in concordance. Group 3 showed more conflict than the other two groups, and there was a discordance between the quantitative and qualitative sets.The value clarity subscale informs on how clear it is to decide which benefits and risks matter more and how clearly it is to know what is most important (the benefits or the risks). This subscale had the lowest conflict score among all of the DCS subscales  and no significant differences were found between groups . One participant (14%) added that the low certainty of the evidence was confusing when carrying out the tradeoffs of the decision. In Group 2, the majority (57%) used the content of the health states to know what benefits and what risks matter most; some participants (43%) highlighted the use of the value elicitation exercises to help them weigh the pros and cons of each health state and clarify what was more important. In Group 3, there was a discrepancy between the quantitative and the qualitative data; while this group showed the highest conflict for this subscale  of all three groups, their qualitative perceptions were that it was clear to them what matters most, risks or benefits. In addition, half of the participants (50%) reported the decision analysis model was useful and helpful to resolve imbalances and make a final decision.the support given by others” and “if there was any pressure by others when making the decision”. We only included the item regarding the amount of advice and information about the choices. The mean score for this subscale showed no conflict  and there were no significant differences between groups (H(2) = 1.7; p = .43). Group 1 and Group 2 showed low conflict  and Group 3 showed no conflict score (< 25). For all groups, there was concordance between the quantitative and qualitative results.This subscale provides information about the support in the process of decision-making and contains three items. The first two items of the subscale were excluded from the analysis as they were questions around “The majority of participants in Group 1 (86%) agreed that the amount of information was adequate; however, the low certainty of the evidence suggests the need for further research on the efficacy of the drug. Most of the participants in Group 2 (71%) stated that the information was adequate. Two participants in Group 2 (29%) that had higher levels of conflict  expressed the need to receive support from their health professional to understand the information given on health states and address other concerns the women could present in this context. Group 3 showed no conflict in this subscale and the majority agreed (75%) that the amount of the information. Two participants reported that the information given by the decision analysis supported their decision.H(2) = .27; p = .87). Quantitative and qualitative results were concordant for Group 1 and 3, but discordant for Group 2.This subscale reflects on certainty of which choice is best for the patient and how easy it is to make the decision. This subscale showed high conflict score , with Group 1 and Group 2 presenting higher conflict than Group 3; however, there were no significant differences between groups . Also, some participants (57%) found it difficult to decide due to the adverse effects presented in their previous experience. Group 2 participants, except one (14%), expressed certainty about their choice, and they all reported that the value elicitation exercises eased the weighing of the benefits and drawbacks of each option. In addition, two participants (29%) noted the low quality of the evidence that was used to inform the outcomes as a basis for their uncertainty. Group 3 participants highlighted that the provided evidence-based information reduced their uncertainties with the decision, and it was clear what choice was best: for the majority (88%), this decision was not to take LMWH due to the low benefits in reducing the risk of a VTE event. Two participants reflected on the decision analysis component and gave discordant perspectives: while one thought that not having a clear recommendation on whether to take LMWH (recommendation shown in the yellow area) introduced uncertainty in the decision-making process , the other thought that this made it easier to confirm their decision, and that there was not a “correct option” (P10 with low conflict > 25-<37.5).H(2) = .69; p = .71), although Group 1 and Group 2 presented higher conflict than Group 3. For all groups, there was concordance between the quantitative and qualitative results.This subscale reflects the final decision and whether choices were well-informed, which points to the level of satisfaction with the process. The mean score for this subscale showed no conflict , and there were no significant differences between groups  felt that the value elicitation exercises did not include aspects that were important in their decision-making (such as long-term effects). In Group 3, we found there was better satisfaction (no conflict < 25) with the process compared to the other two groups (> 25). Some participants (50%) highlighted the role of the value elicitation exercises to feel more involved in the decision-making process; however, other participants (25%) thought the exercises could be confusing (low conflict > 25-<37.5)), especially the VAS . Although all participants from Group 3 responded that the decision analysis was useful to confirm their decision, half of the participants (50%) would not trust the decision analysis model alone to make the final decision; they would prioritize their own desires and would appreciate the support of their health professional.In Table In Table In this study, we aimed to report participant’s perspectives on using an SDM intervention to reduce decisional conflict of a preference-sensitive decision; the use of thromboprophylaxis during pregnancy. Our main finding is that all three components of the SDM intervention  reduce the overall decisional conflict. We found no decisional conflict (DCS < 25) for the overall DCS score and the majority of the subscales, for all three groups; however, this trend was not statistically significant. We found a high level of decisional conflict (> 37.5) for the uncertainty subscale due to the low certainty of the evidence, however value elicitation exercises showed to have the potential to reduce the uncertainty of the process.Evidence-based information reduced conflict in the decision-making process. However, when the information is based on low quality evidence, uncertainty increases. A similar study conducted in Spain , assessiValue elicitation exercises were useful to understand what is most important (risks or benefits) in a decision, thus supporting and facilitating the weighing activity (pros and cons) in the decision-making process. These exercises also reduced uncertainty in the decision-making process by helping participants better clarify (‘what choice is best for me’) their decision. This finding is consistent with IPDAS recommendations , 15. FurThe provision of a decision analysis recommendation decreased uncertainty  and improved self-efficacy with the decision process. It helped tip the balance of pros and cons, helping participants to be more confident with their decision. In addition, participants noted the need for health professionals when implementing the decision analysis technique in the clinical encounter, to support the cognitively-demanding activity of integrating the evidence with their preferences –40. On tIn the context of SDM, decisional conflict is one of the most frequently reported outcomes in studies assessing decision support interventions , 21, 32,The different DCS subscales have normally been compared in relation to usual care , 21, 44,Our sample consisted of students enrolled in a master program and, therefore, we cannot extrapolate our results to the target population of women with a previous VTE event. This limitation was partly due to the COVID-pandemic, which hindered the recruitment of participants . TherefoUsing a mixed method approach, and presenting the data in a joint manner, are some of the main strengths of our study. As other authors , 26, 48 We highlight four main implications of our study that should be addressed in future research and clinical practice:First, high certainty of the evidence is needed to construct decision aids that aim to improve informed decision-making. This is especially important and challenging when there is equipoise regarding the efficacy of alternative treatments. Hence, more studies with larger sample sizes are needed to assess women’s values and preferences for the use of LMWH in pregnancy, thus providing high quality evidence to develop SDM interventions.Second, we highlight the importance of including components that specifically explore patients’ values and preferences, such as value elicitation exercises, to reduce decisional conflict. Simple exercises exploring factors such as their previous experience with the condition or treatment, should be included in the development of SDM interventions.Third, decision analysis has the potential to add value by reducing uncertainty and improving the efficacy and satisfaction with the SDM process. The cognitive reasoning activity of balancing pros and cons could be eased by an algorithm  that combines preferences with evidence. More implementation research is needed on how to deliver the decision analysis recommendation in clinical practice.Fourth, it is essential to assess the interaction between patient and health professional, as well as include health professionals in the development of SDM tools  to betteAll three components of the DASH-TOP intervention  can reduce decisional conflict and improve the quality in the decision-making process. The presentation of patient-tailored decision analytic results helped subjects better understand tradeoffs between risks and benefits of treatment alternatives, and provided an added value to the decision-making process. However, presenting results in real-world clinical settings remains a challenge.Additional file 1: Table S1. Open-ended questionnaire script for qualitative data collection.Additional file 2: Table 1.S2. Participant Characteristics. Table 2.S2. Quantitative Data Collection. Table 3.S2. Quantitative Data Analysis. Table 4.S2. Qualitative Data Collection. Table 5.S2. Qualitative Data Analysis.