Phenotypic overlap between rare disease patients and variant carriers in a large population cohort informs biological mechanisms.

The biological mechanisms giving rise to the extreme symptoms exhibited by rare disease patients are complex, heterogenous, and difficult to discern. Understanding these mechanisms is critical for developing treatments that address the underlying causes of diseases rather than merely the presenting symptoms. Moreover, the same dysfunctional biological mechanisms implicated in rare recessive diseases may also lead to milder and potentially preventable symptoms in carriers in the general population. Seizures are a common, extreme phenotype that can result from diverse and often elusive biological pathways in patients with ultrarare or undiagnosed disorders. In this pilot study, we present an approach to understand the biological pathways leading to seizures in patients from the Undiagnosed Diseases Network (UDN) by analyzing aggregated genotype and phenotype data from the UK Biobank (UKB). Specifically, we look for enriched phenotypes across UKB participants who harbor rare variants in the same gene known or suspected to be causally implicated in a UDN patient's recessively manifesting disorder. Analyzing these milder but related associated phenotypes in UKB participants can provide insight into the disease-causing molecular mechanisms at play in the rare disease UDN patient. We present six vignettes of undiagnosed patients experiencing seizures as part of their recessive genetic condition, and we discuss the potential mechanisms underlying the spectrum of symptoms associated with UKB participants to the severe presentations exhibited by UDN patients. We find that in our set of rare disease patients, seizures may result from diverse, multi-step pathways that involve multiple body systems. Analyses of large-scale population cohorts such as the UKB can be a critical tool to further our understanding of rare diseases in general.

Fever and Increased Gastrointestinal Uptake on Positron Emission Tomography after Anti-Tumour Necrosis Factor Therapy: A Case Report of Whipple's Disease.

Introduction: Whipple's disease is a rare condition that can present with atypical and non-specific features requiring a high index of suspicion for diagnosis. Case Presentation: We present a case of a man in his 40s with peripheral arthritis and bilateral sacro-ileitis for 4-5 years that was treated with an anti-tumour necrosis factor therapy, which led to worsening of his symptoms, elevation of the inflammatory markers, and the development of fever, night sweats, anorexia, and a significant weight loss. The patient had no abdominal pain, diarrhoea, or other gastrointestinal symptoms. An FDG-PET scan showed increased uptake in the stomach and caecum. Endoscopic examination showed inflammatory changes in the stomach and normal mucosa of the duodenum, jejunum, terminal ileum, caecum, and colon. Histopathology was inconclusive, but the diagnosis was confirmed with Tropheryma whipplei PCR testing. He had no neurological symptoms, but cerebrospinal fluid Tropheryma whipplei PCR was positive. He was treated with intravenous ceftriaxone 2 g daily for 4 weeks, followed by trimethoprim/sulfamethoxazole 160/800 mg twice daily for 1 year with close monitoring and follow-up. Conclusion: This case presents an atypical and challenging presentation of Whipple's disease and the importance of proactive testing for neurological involvement.

Prescribed psychostimulants and other pro-cognitive medications in bipolar disorder: A systematic review and meta-analysis of recurrence of manic symptoms.

OBJECTIVES: Clinicians are often hesitant to prescribe psychostimulants in bipolar disorder (BD) due to concerns of inducing (hypo)mania, despite limited published evidence on associations between prescribed psychostimulant use and recurrence of mood episodes in BD. The current systematic review and meta-analysis evaluated the emergence of (hypo)manic symptoms in patients with BD receiving prescribed psychostimulants or other pro-cognitive medications in euthymic or depressive states. METHODS: A systematic search was performed of MEDLINE, Embase, and PsychINFO from inception to April 5, 2023 and search of Clinicaltrials.gov and Clinicaltrialsregister.eu for unpublished data. References of included studies were hand-searched. Randomized trials and prospective longitudinal studies that evaluated psychostimulants and non-stimulant medications recommended for the treatment of ADHD by the Canadian ADHD practice guidelines were included. The review was reported in line with PRISMA guidelines and was preregistered on PROSPERO (CRD42022358588). RESULTS: After screening 414 unique records, we included 27 studies, of which five reported data that was quantitatively synthesized (n = 1653). The use of psychostimulants in BD was not associated with increased scores on the Young Mania Rating Scale in patients who were in a euthymic or depressed state (SMD IV -0.17; 95% CI, -0.40 to 0.06) compared to placebo. There was a high degree of study-level heterogeneity (I2 = 80%). A qualitative synthesis of studies revealed a limited risk of medication-induced manic symptoms. CONCLUSIONS: Our review provides preliminary evidence to suggest psychostimulants and non-stimulant ADHD medications have a limited risk of precipitating (hypo)mania symptoms. More extensive studies evaluating the safety and efficacy of these medications are warranted.

Emerging therapeutic drug monitoring technologies: considerations and opportunities in precision medicine.

In recent years, the development of sensor and wearable technologies have led to their increased adoption in clinical and health monitoring settings. One area that is in early, but promising, stages of development is the use of biosensors for therapeutic drug monitoring (TDM). Traditionally, TDM could only be performed in certified laboratories and was used in specific scenarios to optimize drug dosage based on measurement of plasma/blood drug concentrations. Although TDM has been typically pursued in settings involving medications that are challenging to manage, the basic approach is useful for characterizing drug activity. TDM is based on the idea that there is likely a clear relationship between plasma/blood drug concentration (or concentration in other matrices) and clinical efficacy. However, these relationships may vary across individuals and may be affected by genetic factors, comorbidities, lifestyle, and diet. TDM technologies will be valuable for enabling precision medicine strategies to determine the clinical efficacy of drugs in individuals, as well as optimizing personalized dosing, especially since therapeutic windows may vary inter-individually. In this mini-review, we discuss emerging TDM technologies and their applications, and factors that influence TDM including drug interactions, polypharmacy, and supplement use. We also discuss how using TDM within single subject (N-of-1) and aggregated N-of-1 clinical trial designs provides opportunities to better capture drug response and activity at the individual level. Individualized TDM solutions have the potential to help optimize treatment selection and dosing regimens so that the right drug and right dose may be matched to the right person and in the right context.

Disease progression strikingly differs in research and real-world Parkinson's populations.

Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data, and advances in natural language processing, particularly large language models, allow for a more granular comparison of populations than previously possible. This study includes two research populations and two real-world data-derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using a manually validated natural language processing with a large language model to extract measurements of PD progression. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p < 0.001; mini-mental state exam median decline 0.28 vs. 0.11, p < 0.001; and clinically recognized cognitive decline, p = 0.001). In real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p < 0.001). After diagnosis, in real-world cohorts, treatment with PD medications has initiated an average of 2.3 years later (95% CI: [2.1-2.4]; p < 0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real-world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.

The Utilization of Press-Fit Total Knee Arthroplasty is Not Evenly Distributed: A National Registry Review.

BACKGROUND: This study evaluates trends of cemented versus press-fit total knee arthroplasty (TKA). We hypothesized that press-fit TKA is more common in younger and obese patients. There may also be racial, geographic, and institutional variation. METHODS: The American Joint Replacement Registry was used to conduct a retrospective review of primary TKA procedures for osteoarthritis in the United States between January 2019 and March 2022. The objective was to identify differences in incidence, demographics, body mass index (BMI), Charlson Comorbidity Index (CCI), and institutional teaching status (teaching vs. non-teaching) between press-fit and cemented TKAs. RESULTS: Two hundred ninety-seven thousand four hundred two patients (61% female, average age 68 years, 88.3% White) underwent cemented TKA versus 50,880 patients (52% female, average age 65 years, 89% White) underwent press-fit TKA. Overall, 20.8% of press-fit versus 19.9% of cemented TKA had a BMI of 35 to 39.9 and 15.2% of press-fit versus 12.5% of cemented TKA had BMI >40 ( P < 0.001). Patients undergoing press-fit TKA were less likely Black (OR = 0.727; P < 0.0001), Asian (OR = 0.651, P < 0.0001), and Native Hawaiian/other Pacific Islander (OR = 0.705, P < 0.02) with White as the reference group. Northeastern and Southern United States were more likely to use press-fit TKA than the Midwest (OR = 1.89 and OR = 1.87, P < 0.0001) and West (OR = 1.67; and OR = 1.65; P < 0.0001). Press-fit TKA incidence in 2019 was 9.9% versus 20.6% in 2022 ( P < 0.001). CONCLUSION: Press-fit TKA is increasingly more common in Northeastern and Southern United States, and patients are older than expected. Patients with BMI >35 had a slightly higher rate of undergoing press-fit than cemented TKA. Notable racial differences also exist. Additional research addressing racial disparities and evaluating longevity of press-fit designs is needed.

Disease progression strikingly differs in research and real-world Parkinson's populations.

Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data and recent advances in natural language processing, particularly large language models, allow for a more granular comparison of populations and the methods of data collection describing these populations than previously possible. This study includes two research populations and two real-world data derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using natural language processing with a large language model to extract measurements of PD progression. This extraction process is manually validated for accuracy. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p<0.001; mini-mental state exam median decline 0.28 vs. 0.11, p<0.001; and clinically recognized cognitive decline, p=0.001). In the real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p<0.001). After diagnosis, in real-world cohorts, treatment with PD medications is initiated 2.3 years later on average (95% CI: [2.1-2.4]; p<0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging using existing data. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.

Explaining predictors of discharge destination assessed along the patients' acute stroke journey.

INTRODUCTION: Accurate prediction of outcome destination at an early stage would help manage patients presenting with stroke. This study assessed the predictive ability of three machine learning (ML) algorithms to predict outcomes at four different stages as well as compared the predictive power of stroke scores. METHODS: Patients presenting with acute stroke to the Canberra Hospital between 2015 and 2019 were selected retrospectively. 16 potential predictors and one target variable (discharge destination) were obtained from the notes. k-Nearest Neighbour (kNN) and two ensemble-based classification algorithms (Adaptive Boosting and Bootstrap Aggregation) were employed to predict outcomes. Predictive accuracy was assessed at each of the four stages using both overall and per-class accuracy. The contribution of each variable to the prediction outcome was evaluated by the ensemble-based algorithm and using the Relief feature selection algorithm. Various combinations of stroke scores were tested using the aforementioned models. RESULTS: Of the three ML models, Adaptive Boosting demonstrated the highest accuracy (90%) at Stage 4 in predicting death while the highest overall accuracy (81.7%) was achieved by kNN (k=2/City-block distance). Feature importance analysis has shown that the most important features are the 24-hour Scandinavian Stroke Scale (SSS) and 24-hour National Institutes of Health Stroke Scale (NIHSS) scores, dyslipidaemia, hypertension and premorbid mRS score. For the initial and 24-hour scores, there was a higher correlation (0.93) between SSS scores than for NIHSS scores (0.81). Reducing the overall four scores to InitSSS/24hrNIHSS increased accuracy to 95% in predicting death (Adaptive Boosting) and overall accuracy to 85.4% (kNN). Accuracies at Stage 2 (pre-treatment, 11 predictors) were not far behind those at Stage 4. CONCLUSION: Our findings suggest that even in the early stages of management, a clinically useful prediction regarding discharge destination can be made. Adaptive Boosting might be the best ML model, especially when it comes to predicting death. The predictors' importance analysis also showed that dyslipidemia and hypertension contributed to the discharge outcome even more than expected. Further, surprisingly using mixed score systems might also lead to higher prediction accuracies.

Racial Disparities in Total Knee and Hip Arthroplasty in a Medically Underserved Community with a Diverse Population.

INTRODUCTION: Previous studies have demonstrated lower total joint arthroplasty utilization rates and worse postoperative outcomes among non-White patients. Our study examined whether these disparities exist in the setting of a diverse population. METHODS: This retrospective study included patients with a self-reported race who underwent total knee (TKA) or hip (THA) arthroplasty procedures in a racially diverse county. Patients who did not identify as White or Hispanic/Latino were excluded from the study due to small sample sizes. Demographic, intra and postoperative outcome differences were calculated. A multivariate logistic regression was developed to examine the association between patients' race and undesired postoperative outcomes. RESULTS: Five hundred fifty-five patients were included in our study with 490 identifying as non-Hispanic/Latino White (88.8%) and 65 as Hispanic/Latino (11.2%). Hispanic/Latino-identifying patients were significantly younger (61.9 ± 12.79 versus 68.58 ± 9.00 years), had lower Charlson Comorbidity Index scores, and were more likely to use non-Medicare/Medicaid insurance. We observed no differences between our cohorts in postoperative adverse events, emergency department visits, and hospital readmissions. Patients' self-identified race was not correlated with undesired postoperative outcomes. CONCLUSIONS: Although Hispanic/Latino-identifying patients constitute 50.2% of the county population of our study cohort, they accounted for only 11.2% of the patients in our study. This is noteworthy considering the lack of evidence suggesting a decreased prevalence of osteoarthritis among individuals of different races and ethnicities. Further, the demographic differences we observed suggest an exclusive Hispanic/Latino patient population utilizing TKA or THA procedures. Future studies controlling for risk factors and less invasive treatment options may explain these disparities.

Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study.

BACKGROUND: The evaluation and management of first-time seizure-like events in children can be difficult because these episodes are not always directly observed and might be epileptic seizures or other conditions (seizure mimics). We aimed to evaluate whether machine learning models using real-world data could predict seizure recurrence after an initial seizure-like event. METHODS: This retrospective cohort study compared models trained and evaluated on two separate datasets between Jan 1, 2010, and Jan 1, 2020: electronic medical records (EMRs) at Boston Children's Hospital and de-identified, patient-level, administrative claims data from the IBM MarketScan research database. The study population comprised patients with an initial diagnosis of either epilepsy or convulsions before the age of 21 years, based on International Classification of Diseases, Clinical Modification (ICD-CM) codes. We compared machine learning-based predictive modelling using structured data (logistic regression and XGBoost) with emerging techniques in natural language processing by use of large language models. FINDINGS: The primary cohort comprised 14 021 patients at Boston Children's Hospital matching inclusion criteria with an initial seizure-like event and the comparison cohort comprised 15 062 patients within the IBM MarketScan research database. Seizure recurrence based on a composite expert-derived definition occurred in 57% of patients at Boston Children's Hospital and 63% of patients within IBM MarketScan. Large language models with additional domain-specific and location-specific pre-training on patients excluded from the study (F1-score 0·826 [95% CI 0·817-0·835], AUC 0·897 [95% CI 0·875-0·913]) performed best. All large language models, including the base model without additional pre-training (F1-score 0·739 [95% CI 0·738-0·741], AUROC 0·846 [95% CI 0·826-0·861]) outperformed models trained with structured data. With structured data only, XGBoost outperformed logistic regression and XGBoost models trained with the Boston Children's Hospital EMR (logistic regression: F1-score 0·650 [95% CI 0·643-0·657], AUC 0·694 [95% CI 0·685-0·705], XGBoost: F1-score 0·679 [0·676-0·683], AUC 0·725 [0·717-0·734]) performed similarly to models trained on the IBM MarketScan database (logistic regression: F1-score 0·596 [0·590-0·601], AUC 0·670 [0·664-0·675], XGBoost: F1-score 0·678 [0·668-0·687], AUC 0·710 [0·703-0·714]). INTERPRETATION: Physician's clinical notes about an initial seizure-like event include substantial signals for prediction of seizure recurrence, and additional domain-specific and location-specific pre-training can significantly improve the performance of clinical large language models, even for specialised cohorts. FUNDING: UCB, National Institute of Neurological Disorders and Stroke (US National Institutes of Health).

Zero-shot interpretable phenotyping of postpartum hemorrhage using large language models.

Many areas of medicine would benefit from deeper, more accurate phenotyping, but there are limited approaches for phenotyping using clinical notes without substantial annotated data. Large language models (LLMs) have demonstrated immense potential to adapt to novel tasks with no additional training by specifying task-specific instructions. Here we report the performance of a publicly available LLM, Flan-T5, in phenotyping patients with postpartum hemorrhage (PPH) using discharge notes from electronic health records (n = 271,081). The language model achieves strong performance in extracting 24 granular concepts associated with PPH. Identifying these granular concepts accurately allows the development of interpretable, complex phenotypes and subtypes. The Flan-T5 model achieves high fidelity in phenotyping PPH (positive predictive value of 0.95), identifying 47% more patients with this complication compared to the current standard of using claims codes. This LLM pipeline can be used reliably for subtyping PPH and outperforms a claims-based approach on the three most common PPH subtypes associated with uterine atony, abnormal placentation, and obstetric trauma. The advantage of this approach to subtyping is its interpretability, as each concept contributing to the subtype determination can be evaluated. Moreover, as definitions may change over time due to new guidelines, using granular concepts to create complex phenotypes enables prompt and efficient updating of the algorithm. Using this language modelling approach enables rapid phenotyping without the need for any manually annotated training data across multiple clinical use cases.

Protocol for a systematic review and meta-analysis of coordinate-based network mapping of brain structure in bipolar disorder across the lifespan.

BACKGROUND: Studies about brain structure in bipolar disorder have reported conflicting findings. These findings may be explained by the high degree of heterogeneity within bipolar disorder, especially if structural differences are mapped to single brain regions rather than networks. AIMS: We aim to complete a systematic review and meta-analysis to identify brain networks underlying structural abnormalities observed on T1-weighted magnetic resonance imaging scans in bipolar disorder across the lifespan. We also aim to explore how these brain networks are affected by sociodemographic and clinical heterogeneity in bipolar disorder. METHOD: We will include case-control studies that focus on whole-brain analyses of structural differences between participants of any age with a standardised diagnosis of bipolar disorder and controls. The electronic databases Medline, PsycINFO and Web of Science will be searched. We will complete an activation likelihood estimation analysis and a novel coordinate-based network mapping approach to identify specific brain regions and brain circuits affected in bipolar disorder or relevant subgroups. Meta-regressions will examine the effect of sociodemographic and clinical variables on identified brain circuits. CONCLUSIONS: Findings from this systematic review and meta-analysis will enhance understanding of the pathophysiology of bipolar disorder. The results will identify brain circuitry implicated in bipolar disorder, and how they may relate to relevant sociodemographic and clinical variables across the lifespan.

The Importance of Research Experience With a Scoreless Step 1: A Student Survey at a Community-Based Medical School.

PURPOSE: As of January 26, 2022, the United States Medical Licensing Examination (USLME) step 1 exam went from a scored test to pass-fail step 1 (PFS1). The authors were interested in surveying medical students at a community-based medical school to observe their perceptions of the importance of student research given this recent change. METHOD: A Qualtrics survey was disseminated to medical students (years 1-4) via school emails. Data were analyzed using the Mann-Whitney test to assess Likert scale scores, and narrative comments were grouped as qualitative feedback. Survey dissemination and analysis of data were both conducted at a large community-based medical school. RESULTS: The survey sampled 104 students categorized into pre-clerkship (PC) and clerkship (CL) years, with a response rate of 33%. A contradiction was found, as indicated by the higher number (p = 0.047) of clerkship students interested in Primary Care/Family medicine residency compared to pre-clerkship students at 41% and 59%, respectively. Whereas participants who indicated they are interested in pursuing a competitive specialty for residency were 51% of pre-clerkship students over 41% of clerkship students (p = 0.047). Additionally, given the assessment change to pass/fail, students did in fact believe that residencies would now view research as a higher assessed component than before (79% pre-clerkship and 72% clerkship). However, a minority of students said that they increased their research efforts (41% and 47%). Most students supported the research opportunity improvements proposed in our survey. CONCLUSIONS: Efforts to make the step 1 exam pass/fail may have alleviated some stress related to performance but may have increased the perception of the importance of other components in a student's residency application. Our survey highlights how medical students at a community-based medical school perceive this change and how it has affected their research efforts.

Validating a measure of motivational climate in health science courses.

PURPOSE: The aim of the study was to examine the validity evidence for the 19-item form of the MUSIC Model of Academic Motivation Inventory (College Student version) within health science schools in three different countries. The MUSIC Inventory includes five scales that assess the motivational climate by measuring students' perceptions related to five separate constructs: empowerment, usefulness, success, interest, and caring. BACKGROUND: The 26-item form of the MUSIC Inventory has been validated for use with undergraduate students and with students in professional schools, including students at a veterinary medicine school, a pharmacy school, and a medical school. A 19-item form of the MUSIC Inventory has also been validated for use with undergraduate students, but it has not yet been validated for use with medical school students. The purpose of this study was to provide validity evidence for the use of the 19-item form in heath science schools in three different countries to determine if this version is acceptable for use in different cultures. If validated, this shorter form of the MUSIC Inventory would provide more differentiation between the Interest and Usefulness scales and could reduce respondent fatigue. METHODOLOGY: Cook et al's [1] practical guidelines were followed to implement Kane's [2] validity framework as a means to examine the evidence of validity through scoring inferences, generalization inferences, and extrapolation inferences. Students (n = 667) in health science schools within three countries were surveyed. RESULTS: The results produced evidence to support all five hypotheses related to scoring, generalization, and extrapolation inferences. CONCLUSIONS: Scores from the 19-item form of the MUSIC Inventory are valid for use in health science courses within professional schools in different countries. Therefore, the MUSIC Inventory can be used in these schools to assess students' perceptions of the motivational climate.

Beyond Psilocybin: Reviewing the Therapeutic Potential of Other Serotonergic Psychedelics in Mental and Substance Use Disorders.

There has been a resurgence of interest in the use of psychedelic therapies for several mental and substance use disorders. Psilocybin, a "classic" serotonergic psychedelic, has emerged as one of the primary compounds of interest in clinical research. While research on psilocybin's potential mental health benefits has grown, data on the safety and efficacy of other serotonergic psychedelics remain limited. A comprehensive scoping review on the use of mescaline, ibogaine, ayahuasca, N,N-dimethyltryptamine (DMT), and lysergic acid diethylamide (LSD) in the treatment of mental and substance disorders was conducted. Independent reviewers screened titles, abstracts, and full texts and conducted data extraction. Seventy-seven studies met the inclusion criteria. There were 43 studies of LSD, 24 studies of ayahuasca, 5 studies of DMT, 5 studies of ibogaine, and 5 studies of mescaline. Commonly reported benefits included improved mood and anxiety symptoms, improved insight, reduced substance use, improved relationships, and decreased vegetative symptoms. Commonly reported adverse effects were psychological, neurological, physical, and gastrointestinal in nature. Serious adverse events (homicide and suicide) were reported in published studies of LSD. In conclusion, there is only low-level evidence to support the safety and efficacy of non-psilocybin serotonergic psychedelics in individuals with mental and substance use disorders.

Boxing as an Intervention in Mental Health: A Scoping Review.

Introduction: Physical activity has been shown to have a multitude of mental health benefits. However, there is limited evidence on the specific mental health benefits of boxing. We conducted a scoping review of academic and grey literature to map research of boxing exercises as an intervention in mental health and to identify gaps in knowledge. Methods: The authors utilized the PRISMA-ScR methodological approach and guidelines from the Joanna Briggs Institute and a structured search was completed from inception until August 08, 2022. Results: We identified 16 documents that used non-contact boxing as an exercise intervention that improved various mental health difficulties. Non-contact boxing exercises, usually in a high-intensity-interval training group setting, provided significant reduction in symptoms of anxiety, depression, PTSD and negative symptoms of schizophrenia. Non-contact boxing provided a cathartic release of anger and stress, with evidence of improved mood, self-esteem, confidence, concentration, metabolic burden, strength and coordination. Conclusions: Preliminary evidence indicates that non-contact boxing exercises are a promising intervention to improve mental health burden. Further well designed randomized controlled trials using group, non-contact boxing exercises as an intervention for common mental disorders are warranted to confirm its benefits for mental health.

Zero-shot Interpretable Phenotyping of Postpartum Hemorrhage Using Large Language Models.

Many areas of medicine would benefit from deeper, more accurate phenotyping, but there are limited approaches for phenotyping using clinical notes without substantial annotated data. Large language models (LLMs) have demonstrated immense potential to adapt to novel tasks with no additional training by specifying task-specific i nstructions. We investigated the per-formance of a publicly available LLM, Flan-T5, in phenotyping patients with postpartum hemorrhage (PPH) using discharge notes from electronic health records ( n =271,081). The language model achieved strong performance in extracting 24 granular concepts associated with PPH. Identifying these granular concepts accurately allowed the development of inter-pretable, complex phenotypes and subtypes. The Flan-T5 model achieved high fidelity in phenotyping PPH (positive predictive value of 0.95), identifying 47% more patients with this complication compared to the current standard of using claims codes. This LLM pipeline can be used reliably for subtyping PPH and outperformed a claims-based approach on the three most common PPH subtypes associated with uterine atony, abnormal placentation, and obstetric trauma. The advantage of this approach to subtyping is its interpretability, as each concept contributing to the subtype determination can be evaluated. Moreover, as definitions may change over time due to new guidelines, using granular concepts to create complex phenotypes enables prompt and efficient updating of the algorithm. Using this lan-guage modelling approach enables rapid phenotyping without the need for any manually annotated training data across multiple clinical use cases.

Better flexion and early recovery with medial-stabilized vs single-radius total knee arthroplasty with kinematic alignment: Two-year clinical results.

BACKGROUND: There are few studies comparing outcomes in patients with posterior cruciate ligament-sacrificing single-radius (SR) versus medial-stabilized (MS) knee devices. Both types of implants are designed to maximize deep-flexion and to maintain stability throughout the knee flexion arc. The aim of this study was to determine whether two-year outcomes differ between these two implant groups. METHODS: Two-hundred and ten patients took part in this retrospective cohort single center study. The SR patients (n = 109) were enrolled in one randomized trial, and the MS knees (n = 101) in another. Patient consent and Investigative Review Board approval was obtained. Radiographs and clinical outcomes were gathered preoperatively and at six weeks, six months, one year and two years. RESULTS: There were no statistically significant differences between treatment groups in terms of preoperative demographic characteristics. The MS group had significantly better knee flexion starting at six months postoperative through two years postoperatively (p < 0.05 - p< 0.001). The Knee Society Pain/Motion score was better in the MS group at one year (95.41 vs 90.86, p < 0.002). The Knee Society Pain score was also better in the MS group starting at six weeks through one year (six weeks: 35.3 vs 30, p = 0.007; one year: 46.4 vs 42.4, p = 0.005, respectively). CONCLUSION: The MS group had better clinical outcomes than the SR group, with significantly greater knee flexion from six months through two years, better Knee Society Pain scores at six weeks through one year, and higher Knee Society Pain/Motion scores at six weeks and one year postoperatively. LEVEL OF EVIDENCE: I.