Physician Support Research Articles

An EMR-Based Approach to Determine Frequency, Prescribing Pattern, and Characteristics of Patients Receiving Drugs with Pharmacogenomic Guidelines.

(1) Background: This retrospective analysis utilizing electronic medical record (EMR) data from a tertiary integrated health system sought to identify patients and prescribers who would benefit from pharmacogenomic (PGx) testing based on Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. (2) Methods: EMR data from a clinical research data warehouse were analyzed from 845,518 patients that had an encounter between 2015 and 2019 at an academic medical center. Data were collected for 42 commercially available drugs with 52 evidence-based PGx guidelines from CPIC. Provider data were obtained through the EMR linked by specialty via national provider identification (NPI) number. (3) Results: A total of 845,518 patients had an encounter in the extraction period with 590,526 medication orders processed. A total of 335,849 (56.9%) patients had medication orders represented by CPIC drugs prescribed by 2803 providers, representing 239 distinct medications. (4) Conclusions: The results from this study show that over half of patients were prescribed a CPIC actionable medication from a variety of prescriber specialties. Understanding the magnitude of patients that may benefit from PGx testing, will enable the development of preemptive testing processes, physician support strategies, and pharmacist workflows to optimize outcomes should a PGx service be implemented.

Can the Use of Cardiology Medical Record to Deliver Educational Intervention Improve Care? On Behalf of TAPP Program: Thinking Approach Towards Physician Support in Patient Management

Background: Despite clear and concise practice guidelines, strategies for lowering LDL-C are often poorly adopted in clinical practice, and many patients fail to reach guideline-recommended levels despite physician education and quality improvement programs. We studied whether physician focussed, guideline-based practice level educational intervention can improve lipid lowering management. Methods: Cardiologists or internal medicine specialists from the province of Ontario, Canada who were using a cardiology specific EMR (CEREBRUM, WELL Health Technologies Corporation) were invited to participate. Practice level data of patients with history of acute coronary syndromes (ACS) and lipid profile were studied. Physicians were alerted when patients in their practice were not treated according to recommendations. The primary endpoint was proportion of patients achieving the recommended LDL-C level of below 1.8 mmol/L. Results: Of the invited 378 specialists, 178 agreed to participate and shared their practice involving 7,683 ACS patients who were 70.4 ± 10.3 years of age and 27.2% were women. Overall, 57.7% of patients had LDL-C < 1.8 mmol/L at the start of the program (1.84 ± 0.87 mmol/L) and 63.0% (1.75 ± 0.79 mmol/L) at the end of the program (p<0.0001). With respect to the lipid lowering therapy, statin therapy was used in 52.9% of patients at the start of the program and increased to 72.1% at the end (p<0.0001). The use of ezetimibe increased from 12.6% to 19.0% (p<0.0001) and the use of PCSK9i from 1.2% to 2.4% (p<0.0001). Conclusion: The results indicate the feasibility of using EMR as a platform to deliver educational intervention and overcoming treatment inertia and improving LDL-C lowering.

Patient Admission and Mechanical Ventilator Allocation Decision-Making Processes by Frontline Medical Professionals in a Japanese ICU During the COVID-19 Pandemic: A Qualitative Study.

During the COVID-19 pandemic, the need to triage COVID-19 patients in ICUs emerged globally. Triage guidelines were established in many countries; however, the actual triage decision-making processes and decisions themselves made by frontline medical providers may not have exactly reflected those guidelines. Despite the need to understand decisions and processes in practice regarding patient ICU admission and mechanical ventilator usage to identify areas of improvement for medical care provision, such research is limited. This qualitative study was conducted to identify the decision-making processes regarding COVID-19 patient ICU admissions and mechanical ventilator allocation by frontline medical providers and issues associated with those processes in an ICU during the COVID-19 pandemic. Semi-structured, in-depth interviews were conducted with ICU physicians and nurses working at an urban tertiary referral hospital in Japan between February and April 2022. Patient characteristics that influenced triage decisions made by physicians and the interaction between physicians, nurses, and senior management staff upon making such decisions are discussed in this article. An implicated issue was the lack of legal support for Japanese physicians to practice withdrawal of life-sustaining treatments even during emergencies. Another issue was the impact of non-clinical forces-likely specific to health emergencies-on physicians' decisions regarding mechanical ventilator allocation, where such forces imposed a significant mental burden on the medical providers. We consider public policy and legal implications for future pandemics.

Lived Experiences of Physicians Working in Hospitals during the COVID-19 Crisis: A Qualitative Study

Background: With the onset of the coronavirus pandemic in 2019, healthcare workers were at the forefront of dealing with the disease. Medical centers were faced with limited facilities and insufficient information. A number of medical staff became infected with the disease, and some lost their lives. This study was conducted to understand the perceived impact of the pandemic on physicians working in healthcare facilities of Shiraz and Yazd Universities of Medical Sciences during the COVID-19 pandemic. Methods: The present study was a qualitative one conducted in the teaching hospitals of Shiraz and Yazd in the southwest of Iran in 2020-2021. Purposive sampling was used to select the participants and the process continued until data saturation was reached. Data were collected through semi-structured interviews with 21 physicians working in hospital wards during the COVID-19 crisis. Data were analyzed using the seven-step analytical method proposed by Colaizzi. Results: Four themes and eleven subthemes emerged from data analysis. The main themes included managerial weakness, burnout, distorted doctor-patient relationship, and misinfodemic. Conclusion: The preparedness of the medical system and the infrastructure needed to deal with the coronavirus pandemic were not as expected. The COVID-19 crisis also highlighted the lack of information and support for physicians as a significant challenge.

Target Re-Delineation in Daily Online Adaptive Radiotherapy of Bladder Cancer: AI vs. Adapters

The purpose was to assess the quality of artificial intelligence (AI) generated targets in cone-beam CT (CBCT) guided online adaptive radiotherapy (oART) of bladder cancer and compare it to corresponding contours edited by adapters (radiotherapy technicians performing oART routinely). Furthermore, to assess the inter-observer delineation variation in daily target re-delineation in oART of bladder cancer. Ten consecutive patients treated with oART for muscle-invasive bladder cancer from Feb 2020 to Feb 2021 were retrospectively included. One CBCT randomly selected from each of the 10 clinical treatment courses was exported to an emulator, where delineations and oART simulations were carried out. The bladder was automatically segmented (clinical target volume (CTV)) using AI. Seven adapters independently reviewed and, if necessary, manually edited the AI-generated structure. A ground truth structure was first delineated blindly by a senior oncologist, then reviewed by an oART experienced medical physicist, and finally edited to a consensus structure. Planning CT and MR images with reference delineations were available during all simulations and delineations. The AI-generated and adapter-edited CTVs were compared to ground truth using dice similarity coefficient (DSC) and volume difference. The inter-observer variation in adapter-edited CTVs was assessed using coefficient of variation (CV) and the generalized conformity index (CIgen). Cigen was calculated as the ratio of the sum of all overlapping volumes between pairs of observers and the sum of all overlapping and non-overlapping volumes between the same pairs. Nine CTVs (7 adapter-edited, 1 AI-generated, 1 ground truth) were generated per patient. Patient cases included three male and seven females; three had catheter. The ground truth volume ranged from 51.5 to 221.6 cc. The median volume difference compared to ground truth was -4.5 (range, -17.8 - 42.4) cc and -15.5 (range, -54.2 - 4.3) cc for adapter-edited and AI-generated structures, respectively. Corresponding DSC values were 0.87 (range, 0.71 - 0.95) and 0.84 (range, 0.64 - 0.95). The AI-generated CTV was smaller than ground truth for all patients except one and smaller than all adapter-edited CTVs. The largest differences among adapter-edited CTVs were observed in cranial, caudal and posterior directions. The median CV was 0.08 (range, 0.05-0.11) and Cigen was 0.78 (range, 0.71-0.88). Target re-delineations in daily CBCT-based oART of bladder cancer demonstrated small inter-observer variation. Manual adjustment of AI-generated structures resulted in improved accuracy in target delineation compared to ground truth. Efforts to reduce the inter-observer variation, e.g., physician support during the initial fractions and review by a second adapter, have been implemented clinically.

A Nomogram Based on Radiomics and Genomics Factors for Predicting Radiation Pneumonia in Patients with Limited-Stage Small Cell Lung Cancer after Definitive Chemoradiotherapy

Although definitive chemoradiotherapy (dCRT) is currently the most effective treatment for limited-stage small cell lung cancer (SCLC), some patients may develop severe radiation pneumonia (RP). We aimed to develop a nomogram by combining the clinical, radiomics and genomics factors to predict the RP. Totally 148 limited-stage SCLC patients treated with dCRT were retrospectively enrolled and divided into the training (n = 103) and validation (n = 45) cohorts. All tumor tissue samples were detected by 474 Panel gene sequencing technology. Eight hundred fifty-one radiomics features of lung other than hilum, atelectasis and tumor were extracted from the initial CT images. Least absolute shrinkage and selection operator (LASSO) method was applied to select and build radiomic signature. Combined with genomic, radiomic and clinical features, a nomogram was established by univariate and multivariate analysis. Concordance index (C-index), calibration curve and decision curve were used to evaluate the predictive ability and application value of the nomogram. Six selected radiomic features was used to construct radiomic signature. Multivariate analysis showed that NQO1 gene (OR, 8.354; 95% CI, 1.061-65.765; P = 0.044), radiotherapy technique (OR, 9.586; 95% CI, 1.168-78.70; P = 0.035) and radiomic signature (OR, 5.745; 95% CI, 1.879-17.56; P = 0.002) were independent risk factors for RP. Compared with the single clinical (C-index = 0.621 and 0.533), radiomic (C-index = 0.696 and 0.550) or genetic (C-index = 0.561 and 0.433) factor, the C-index of nomogram was the highest, reaching 0.764 and 0.622 in the training group and validation group, respectively. The calibration curves indicated good agreement between the predictions and actual observations. Moreover, decision curves revealed that the nomogram had high clinical practicability. This study further reveals the potential of radiomics and genomics in predicting the occurrence of RP, and may provide individualized prediction for patients with limited-stage SCLC, as well as theoretical and technical support for physicians to optimize clinical decisions.

Physician beliefs about abortion safety and their participation in abortion care

ObjectiveTo document physicians’ beliefs about abortion safety and the associations between these beliefs and physician support for, referral for, and participation in abortion care. MethodsIn a 2019 survey at the University of Wisconsin School of Medicine and Public Health, we assessed physicians’ abortion attitudes, beliefs, and practices (N = 893). We conducted bivariate analyses followed by logistic regression to document relationships between physician beliefs about abortion safety and their support for, referral to, and participation in abortion care. ResultsFour-in-five physicians (78%, n = 690) believed that abortion is very or extremely safe. Medical specialty (Obstetrics-Gynecology vs. other; adjusted odds ratio [aOR] = 10.58, 95% CI: 1.41–79.56), educational exposure to abortion (aOR = 1.43, 95% CI: 1.02–2.01), and religiosity (aOR = 0.59, 95% CI: 0.41–0.85) were associated with physicians’ beliefs about the safety of abortion. Providers who believed that abortion was very/extremely safe were more likely to support medication (aOR = 2.99, 95% CI: 1.93–4.65) and procedural abortion (aOR = 3.56, 95% CI: 2.31–5.50) and refer patients for abortion care (aOR = 3.14, 95% CI: 1.90–5.01). ConclusionAlthough abortions are associated with extremely few adverse events, a sizable portion of surveyed physicians had incorrect perceptions of the safety of abortion. These beliefs were associated with decreased support and referrals for abortion care. Educational exposure to abortion is associated with more accurate assessments of abortion safety, underscoring the importance of training in this area. Considering the current abortion policy landscape, it is imperative for physicians to hold accurate knowledge about abortion so they can provide comprehensive counseling and, when indicated, referrals for safe and legal care.

Autoimmunity and Infection in Glomerular Disease.

The ongoing glomerular damage of infections is not limited to the most widely known form of post-streptococcal glomerulonephritis, which is today less common in the Western world; other forms of glomerulonephritis are associated with several bacterial, viral and parasitic pathogens. The mechanisms responsible range from the direct damage of glomerular cells to the formation and deposition of immunocomplexes to molecular mimicry to the secretion of superantigens. Similarly, in the course of glomerular disease, infections are more frequent than in the general population due to the loss of immunoglobulins in urine and the immunosuppressive agents used to treat the autoimmune disease that decrease the activity of the immune system. Recognizing this two-way link, understanding its pathogenetic mechanism, and identifying the most appropriate therapeutic choice are essential for the personalized management of patients. In this continuously developing field, this short review summarizes the current state of the art as support for physicians, who are increasingly involved in managing patients with glomerular disease and infections.

Systematic reviews and meta-analyses on androgen administration in humans: an umbrella review.

The present umbrella review systematically searches and synthesizes recent (2021-2023) systematic reviews and meta-analyses (SRMA) on androgen administration in humans. Results from 19 SRMA are synthesized. Four themes were identified: market for androgens, prevalence of androgen abuse, psychological effects of androgens, and clinical/therapeutic effects of androgens. One of three androgens on the illicit market are fake or substandard. Whereas one of three androgen users experiences dependence and various serious disorders, only one of three androgen users seek physician support. Additionally, androgen administration increases self-reported aggression in healthy male individuals, and abuse positively correlates with interpersonal violence. Furthermore, androgen therapy is beneficial in treating persons suffering from various chronic diseases. In this umbrella review, we identified 19 SRMA on androgen use and abuse. Results show a high prevalence of fake androgens on the illicit market, a high prevalence of androgen abuse and dependence, and that a huge proportion of androgen users tend to avoid seeking support from clinicians. Also, androgen administration increases self-reported aggression among healthy male individuals, and there is a positive correlation between androgen abuse and violent behaviors. Finally, androgens have multiple therapeutic effects on various pathology.

A semantic segmentation of the lung nodules using a shape attention-guided contextual residual network

Objective. The early diagnosis of lung cancer depends on the precise segmentation of lung nodules. However, the variable size, uneven intensity, and blurred borders of lung nodules bring many challenges to the precise segmentation of lung nodules. Approach. We propose a shape attention-guided contextual residual network to address the difficult problem in lung nodule segmentation. Firstly, we establish a selective kernel convolution residual module to replace the original encoder and decoder. This module incorporates selective kernel convolution, which automatically selects convolutions with different receptive fields to acquire multi-scale spatial features. Secondly, we construct a multi-scale contextual attention module to assist the network in extracting multi-scale contextual features of local feature maps. Finally, we develop a shape attention-guided module to assist the network to restore details such as the boundary and shape of lung nodules during the upsampling phase. Main results. The proposed network is comprehensively analyzed using the publicly available LUNA16 data set, and an ablation experiment is designed to validate the effectiveness of each individual component. Ultimately, the dice similarity coefficient of the experimental results reaches 87.39% on the test set. Compared to other state-of-the-art segmentation methods, the proposed network achieves superior performance in lung nodule segmentation. Significance. Our proposed network improves the accuracy of lung nodule segmentation, which provides an important support for physicians to subsequently develop treatment plans.

Factors underlying burnout among rural village physicians in Southwestern China

AbstractBackgroundPrimary healthcare doctors in China often experience problems with occupational burnout, a condition known to relate to high job stress and low wages. In China, many medical alliances have recently been established in rural areas, where village physicians work as healthcare gatekeepers. However, burnout in village physicians in the context of medical alliances remains underresearched.MethodsThis cross‐sectional survey was conducted among 100 village physicians practicing at village clinics in Qiandongnan prefecture, Guizhou province, China. An online questionnaire was distributed to assess physicians' demographic characteristics and work situations. Burnout was measured using the Oldenburg Burnout Inventory (validated Chinese version). A multivariate linear model with stepwise procedure was used to estimate the effects of factors of interest on burnout, focusing particularly on actions within the medical alliance that involved respondents' clinics, such as training and support for village physicians provided by higher‐level facilities.ResultsThe overall response rate was 79%. The mean burnout score was 38.09 (standard deviation, 4.55; range, 25–47). The multivariate analysis showed that fewer working years and too much farming work were significantly related to exacerbation of burnout. Greater medical services in the total workload and greater support from higher‐level facilities were associated with burnout alleviation.ConclusionClose connections and interactions across medical alliance member facilities could facilitate reduction in burnout for village physicians practicing as primary care gatekeepers.

Prevalence, characteristics and challenges of late HIV diagnosis in Germany: an expert narrative review.

We aimed to review the landscape of late HIV diagnosis in Germany and discuss persisting and emerging barriers to earlier diagnosis alongside potential solutions. We searched PubMed for studies informing the prevalence, trends, and factors associated with late HIV diagnosis in Germany. Author opinions were considered alongside relevant data. In Germany, older individuals, heterosexuals, and migrants living with HIV are more likely to be diagnosed late. The rate of late diagnosis in men who have sex with men (MSM), however, continues to decrease. Indicator conditions less often prompt HIV testing in women and non-MSM. During the COVID-19 pandemic, the absolute number of late diagnoses fell in Germany, but the overall proportion increased, probably reflecting lower HIV testing rates. The Ukraine war and subsequent influx of Ukrainians living with HIV may have substantially increased undiagnosed HIV cases in Germany. Improved indicator testing (based on unbiased assessments of patient risk) and universal testing could help reduce late diagnoses. In patients who receive a late HIV diagnosis, rapid treatment initiation with robust ART regimens, and management and prevention of opportunistic infections, are recommended owing to severely compromised immunity and increased risks of morbidity and mortality. Joint efforts are needed to ensure that UNAIDS 95-95-95 2030 goals are met in Germany. These include greater political will, increased funding of education and testing campaigns (from government institutions and the pharmaceutical industry), continued education about HIV testing by HIV experts, and broad testing support for physicians not routinely involved in HIV care.

Physician engagement in organisational patient safety through the implementation of a Medical Safety Huddle initiative: a qualitative study

BackgroundEfforts to increase physician engagement in quality and safety are most often approached from an organisational or administrative perspective. Given hospital-based physicians’ strong professional identification, physician-led strategies may offer a...

Heterogeneous temporal representation for diabetic blood glucose prediction.

Background and aims: Blood glucose prediction (BGP) has increasingly been adopted for personalized monitoring of blood glucose levels in diabetic patients, providing valuable support for physicians in diagnosis and treatment planning. Despite the remarkable success achieved, applying BGP in multi-patient scenarios remains problematic, largely due to the inherent heterogeneity and uncertain nature of continuous glucose monitoring (CGM) data obtained from diverse patient profiles. Methodology: This study proposes the first graph-based Heterogeneous Temporal Representation (HETER) network for multi-patient Blood Glucose Prediction (BGP). Specifically, HETER employs a flexible subsequence repetition method (SSR) to align the heterogeneous input samples, in contrast to the traditional padding or truncation methods. Then, the relationships between multiple samples are constructed as a graph and learned by HETER to capture global temporal characteristics. Moreover, to address the limitations of conventional graph neural networks in capturing local temporal dependencies and providing linear representations, HETER incorporates both a temporally-enhanced mechanism and a linear residual fusion into its architecture. Results: Comprehensive experiments were conducted to validate the proposed method using real-world data from 112 patients in two hospitals, comparing it with five well-known baseline methods. The experimental results verify the robustness and accuracy of the proposed HETER, which achieves the maximal improvement of 31.42%, 27.18%, and 34.85% in terms of MAE, MAPE, and RMSE, respectively, over the second-best comparable method. Discussions: HETER integrates global and local temporal information from multi-patient samples to alleviate the impact of heterogeneity and uncertainty. This method can also be extended to other clinical tasks, thereby facilitating efficient and accurate capture of crucial pattern information in structured medical data.

P-621 Machine learning software significantly increase clinical pregnancy rates in natural frozen-thawed embryo transfer cycles

Abstract Study question To study the effect of physician support software for determining ovulation time on pregnancy outcomes in natural cycle frozen embryo transfers (NC-FET) Summary answer Match between the physician and the algorithm regarding the time of ovulation in NC-FET cycles resulted in significant 14.4% increase in clinical pregnancy rates. What is known already Today the preferred method for frozen embryo transfer is natural cycle based on ovulation detection. As of today, an algorithm for predicting ovulation has not been developed and its use and its effect on success rates have not been tested. The aim of this study was to test a physician support artificial intelligence (AI) software for determining ovulation time and to study the effect of its use on pregnancy outcomes in frozen-thawed embryo transfers (NC-FET). Study design, size, duration NC-FET cycles from September 2018 to June 2021 were used to develop ovulation detection algorithm. Next, the effect of the selection of the ovulation date by the algorithm on pregnancy rates was tested by retrospective analysis on NC-FET cycles performed between July 2021 to June 2022. Cycles were divided into two groups: The Matched group in which there was a match between the ovulation date set by the physician and the algorithm and Mismatched group. Participants/materials, setting, methods Study group included 349 single embryo transfer patients after applying strict inclusion criteria. The Matched group included 198 patients and 151 patients in the Mismatched group. Patient's age, BMI, smoking, number of past retrievals and past embryo transfers, endometrium thickness, embryos age and quality, number of tests per cycle and pregnancy rates were compared. Statistical analysis included multivariable logistic regression to study the association between Match/Mismatch and clinical pregnancy rates while controlling for potential confounders Main results and the role of chance Matched group and Mismatched group had similar characteristics. Mean patients age at oocyte retrieval were 34.5 + 5.5 vs. 34.7 + 5.5 respectively (p=0.72). Clinical pregnancy rate in the Matched group were 38.9% compared to 24.4% in the Mismatched group (p =0.006). The difference was particularly noticeable when day 3 embryos were transferred (39.8% vs. 17.6% respectively p=0.009), and was also observed on day 5 embryos transfer, although non-significant (39.1% vs. 30.0% respectively, p=0.197). In the Matched group the chance of pregnancy actually almost doubled (OR = 1.97, CI 95% 1.23-3.17, p=0.005) while controlling potential confounders. Further analysis of the Mismatch group found that in 120 cycles (%) the doctor set the ovulation date one day or more before the date set by the algorithm and in 31(%) cases the date was a day or more after the ovulation date according to the algorithm. Logistic regression showed that the chance of pregnancy was significantly affected when the doctor determines the day of ovulation earlier than the algorithm (OR = 0.42, CI 95% 0.29-1.43, p=0.005) Limitations, reasons for caution Although cycles selected for comparison were controlled for key factors that affect pregnancy outcomes, The main drawback is the retrospective analysis. Further prospective randomized trials are needed to study the effectiveness of the algorithm and its effect on treatment results. Wider implications of the findings This is the first AI algorithm designed to predict ovulation. Using AI algorithms in NC-FET can help in identifying ovulation accurately and reliably and may increase pregnancy rate Trial registration number Not applicable

P-656 Machine learning algorithm automatically manages and accurately predicts ovulation in natural frozen-thawed embryo transfer cycles.

Abstract Study question Can an Artificial Intelligence (AI) algorithm automatically manage frozen-thawed embryo transfer (NC-FET) treatment cycles and give an accurate prediction of ovulation day. Summary answer An AI algorithm automatically managed and predicted the ovulation of NC-FET treatment cycles with 94.8% accuracy using an average of 3.01 test days. What is known already Today the preferred method for frozen embryo transfer is natural cycle based on ovulation detection. Currently, there is no software capable of managing the treatment cycle automatically and identifying the time of ovulation to support doctor decisions. The aim of this study is to develop a physician support AI software for determining ovulation time reliably with high accuracy. Study design, size, duration 2083 NC-FET cycles from September 2018 to June 2021 were used to develop the ovulation detection and treatment management algorithms. Each cycle had data from at least 2 visits including: hormonal levels (Estrogen/Progesterone/LH) and follicle sizes. The dataset was divided into a train set and two test sets. In the 1st test set ovulation was determined by experts’ opinions and the 2nd test set included cycles in which follicle rupture was documented in consecutive ultrasounds. Participants/materials, setting, methods Two algorithms were developed, an ovulation prediction algorithm based on an NGBoost model and a treatment management algorithm that used the model to determine if and when to call for a new test or declare the ovulation day. Both algorithms were jointly tuned to reach the highest success rate, defined as providing the correct day of ovulation using the available cycle data, with as few test days as possible. Main results and the role of chance On the first test set, which consisted of 176 cycles in which ovulation was determined through the majority decision of 2 independent experts and the attending physician, the treatment management algorithm required on average 3.01 tests to reach a prediction and successfully predicted the ovulation day in 94.8% of cycles. In the second test set, which consisted of 29 cycles in which ovulation was determined through the follicular rupture in two consecutive ultrasounds, only the ovulation prediction model was tested. To ensure that the model provides a reliable answer and does not rely solely on the follicular disappearances, examined cycles were tested twice: Once using the ovulation day without the day prior to it, and again using only the day prior to ovulation without the ovulation day itself. The algorithm accurately predicted ovulation in 28 out of 29 instances (96.6%) using the day of ovulation and in 28 out of 29 instances (96.6%) using the day before ovulation. Limitations, reasons for caution The main drawback is this being a retrospective study: while the algorithm was trained to maximize accuracy when it selects the test days, the dataset test days were selected by the attending physicians. Statistical methods were used to overcome this, however further prospective trials are needed to validate the results. Wider implications of the findings This is the first AI algorithm designed to automatically manage NC-FET IVF treatment cycles and predict ovulation. The high accuracy and low average tests count might improve treatment outcomes, reduce the patients’ life disruption, and allow physicians to spend less time monitoring their patients’ treatments. Trial registration number not applicable

Effect of the number of unhealthy lifestyles in middle-aged and elderly people on hypertension and the first occurrence of ischemic stroke after the disease.

To determine the relationship between the number of controllable unhealthy lifestyles on the risk of the first occurrence of ischemic stroke after the disease in middle-aged and elderly people in the community, and to provide data support and basis for community physicians to guide hypertensive patients to control modifiable risk factors to prevent the first occurrence of ischemic stroke. The relationship between the number of unhealthy lifestyles and the risk of hypertension was analyzed by binary logistic regression in 584 subjects using a medical record control study. A retrospective cohort study of 629 hypertensive patients was used to analyze the relationship between the number of unhealthy lifestyles and the risk of the first occurrence of ischemic stroke within 5 years of developing hypertensive disease using Cox proportional risk regression models. Logistic regression model analysis showed that taking an unhealthy lifestyle as a reference, the OR (95% CI) values of, 2, 3, 4 and 5 unhealthy lifestyle were 4.050 (2.595-6.324), 4 (2.251-7.108), 9.297 (3.81-22.686), and 16.806 (4.388-64.365), respectively. Cox Proportional risk regression model analysis showed that the risk of ischemic stroke within 5 years after developing hypertension was referenced to 5 unhealthy lifestyles, and the HR (95% CI) for 3, 2, and 1 unhealthy lifestyle were 0.134 (0.023-0.793), 0.118 (0.025-0.564), and 0.046 (0.008-0.256), respectively. The number of controllable unhealthy lifestyles in middle-aged and elderly people was positively associated with the risk of hypertension and first ischemic stroke after hypertension, and there was a dose-effect relationship between them. The risk of hypertension and first ischemic stroke within 5 years after hypertension onset increased with the number of unhealthy lifestyles.

Electronic Monitoring Of Mom’s Schedule (eMOMSTM): Recruitment of pregnant populations with elevated BMI in a feasibility randomized controlled trial

Underrepresentation of pregnant populations in randomized controlled trials of lifestyle change interventions is concerning due to high attrition and providers’ limited clinical time. The purpose of this evaluative study was to assess intervention uptake of pregnant individuals enrolled in a three-arm feasibility randomized controlled trial, electronic Monitoring Of Mom’s Schedule (eMOMSTM), examining lifestyle changes and lactation support alone, and in combination. Measures included: (1) participation and completion rates, and characteristics of intervention completers versus other eligible participants; and (2) provider experiences with screening and enrolling pregnant participants. Pregnant people with a pre-pregnancy body mass index ≥ 25 and < 35 kg/m2 were enrolled into the eMOMSTM trial between September 2019 - December 2020. Of the 44 consented participants, 35 were randomized, at a participation rate of 35%, and 26 completed the intervention, resulting in a completion rate of 74%. Intervention completers were slightly older and entered the study earlier in pregnancy compared to non-completers. Completers were more likely to be first-time mothers, resided in urban areas, had higher educational attainment, and were slightly more racially and ethnically diverse. A majority of providers reported willingness to participate, believed the study aligned with their organization’s mission, and were satisfied with using iPads for screening. Lessons learned to guide recruitment success include use of: (1) designated research staff in combination with physician support; and (2) user-friendly technology to help mitigate time burden on physicians and their staff. Future work should focus on successful strategies to recruit/retain pregnant populations in clinical trials.

Wart-Treatment Efficacy Prediction Using a CMA-ES-Based Dendritic Neuron Model

Warts are a prevalent condition worldwide, affecting approximately 10% of the global population. In this study, a machine learning method based on a dendritic neuron model is proposed for wart-treatment efficacy prediction. To prevent premature convergence and improve the interpretability of the model training process, an effective heuristic algorithm, i.e., the covariance matrix adaptation evolution strategy (CMA-ES), is incorporated as the training method of the dendritic neuron model. Two common datasets of wart-treatment efficacy, i.e., the cryotherapy dataset and the immunotherapy dataset, are used to verify the effectiveness of the proposed method. The proposed CMA-ES-based dendritic neuron model achieves promising results, with average classification accuracies of 0.9012 and 0.8654 on the two datasets, respectively. The experimental results indicate that the proposed method achieves better or more competitive prediction results than six common machine learning models. In addition, the trained dendritic neuron model can be simplified using a dendritic pruning mechanism. Finally, an effective wart-treatment efficacy prediction method based on a dendritic neuron model, which can provide decision support for physicians, is proposed in this paper.

Stratified Reproduction, Hysterectomy, and the Social Process of Opting into Infertility

Shifting cultural norms transform the uses and meanings of medical practices, and, in turn, medical practices have the capacity to alter social relations. In this article, I use hysterectomy as a case for understanding how reproductive health practices are constrained by and contribute to notions of gender, race, and stratified reproduction. Hysterectomy is regularly performed yet understudied and has been transformed by both technological advances and shifting norms in gender and reproduction. I draw on 100 in-depth interviews with individuals who had, want, or are considering hysterectomy to treat chronic reproductive health conditions or as gender-affirming care for trans and gender-nonbinary (TGNB) individuals. These comparative groups shed insight across three gender groups (cis women, trans men, nonbinary) as well as across race. Findings show divergent patient–provider interactions ranging from physician support to provider coercion to gatekeeping. Similarly, the data reveal that hysterectomy evokes a wide range of reactions—from delight to neutrality to grief. These distinct reactions and interactions map on to gender, race, and ethnicity, revealing persistent reproductive stratification by social positionality. Bringing together feminist science and technology studies with intersectional theories of the body and reproductive justice, I show how stratified reproduction operates when gender identities vary and introduce the concept of opting into infertility.