Citations

ABSTRACT Objective This study aims to explore whether racial identity or socioeconomic status (SES) is associated with growth trajectory before and after palatoplasty. Methods This study was a single‐site retrospective chart review at a tertiary pediatric hospital. This study involved children with cleft palate repaired between 2006 and 2022. Primary outcomes were changes in growth trajectories before and after palatoplasty stratified by race and median neighborhood income (MNI) as a measure of SES. Results In the data query, 187 and 149 patients met the inclusion criteria for the racial identity and MNI analyses, respectively. The median pre‐ and post‐surgical weekly weight gain was 0.11 kg/wk. and 0.05 kg/wk. for patients who are BIPOC (Black, Indigenous, and People of Color) ( p = < 0.0001), and 0.12 and 0.07 kg/wk. for patients who are White ( p = < 0.0001). Change in median growth percentile following palatoplasty was significant for patients who are White but was not significant for patients who are BIPOC. MNI displayed a significant relationship with weekly weight gain and growth percentile changes following palatoplasty. Conclusion Patient racial identity is associated with clinically meaningful differences in growth trajectory outcomes following palatoplasty. While growth outcomes may differ across MNI, living in areas with higher MNI does not directly translate to improved growth trajectories following palatoplasty. Addressing all social determinants of health in this population is critical, but interventions should focus on supporting racial minorities as they experience the most significant disparities. Level of Evidence 3.

Adoptive cell therapy (ACT) has shown remarkable success in the treatment of some malignancies, particularly leukemia. However, there are multiple factors that limit the durability of ACT in solid tumors, including dose-limiting toxicities, the immunosuppressive tumor microenvironment, and T-cell exhaustion. As the manufacture and preparation of adoptive T-cell therapies allows time and adequate conditions for ex vivo T-cell engineering, forward genetic screens can identify novel genetic targets that could improve their effectiveness. CRISPR is a commonly used functional genomics tool that has been successfully used to both enhance our understanding of mechanisms of resistance and to discover potential genetic edits to improve ACT. A complementary approach, Sleeping Beauty transposon mutagenesis provides additional opportunities to identify novel genetic edits without being constrained by the annotated human genome. Here, we summarize forward genetic screens and their tools to uncover strategies to enhance ACT. Complementary approaches can be combined and improved on to identify translatable genetic editing strategies through studies that accurately recapitulate disease-specific challenges.

Abstract Background: For large language models (LLMs) to reach their potential as information technology tools that make medication use safer, clinically relevant benchmarks capable of automated grading and designed specifically to measure the performance of LLMs for medication tasks are required. The purpose of this study was to design a suite of benchmarking tests reflective of Comprehensive Medication Management (CMM; the standard of care for medication optimization) and quantify the baseline performance of the latest LLMs. Methods: We established six benchmarks representing critical stages of the CMM process: drug formulation matching, drug order (sig) generation, drug route matching, drug-drug interaction identification, renal dose identification, and drug-indication matching. For each benchmark, we curated a clinician-annotated dataset comprising 250 standardized input-output pairs including both inpatient and outpatient medications. We evaluated the clinical knowledge retrieval capabilities of three LLMs: GPT-4o-mini, MedGemma-27B, and LLaMA3-70B. We employed a zero-shot prompting strategy, excluding in-context examples, to assess the models’ internal clinical knowledge rather than their few-shot learning potential. To check reliability, each model was run three times using a temperature of 0.7 (a mid-range value of an LLM setting controlling text generation randomness). Performance was assessed using task-specific evaluation metrics including precision (positive predictive value), recall (sensitivity), F1-score, accuracy, and correctness consistency across trials. Results: Across six benchmarks, LLaMA3-70B demonstrated the highest performance in four tasks: drug-formulation matching (F1, 54.0% [95 CI: 50.1-58]), drug-order generation (accuracy, 88.0%), drug-route identification (F1, 74.3% [95 CI: 71-78]), and drug-indication identification (accuracy, 97.6% [95 CI: 95.6-99.2]). In the drug–drug interaction task, GPT-4o-mini achieved the highest overall accuracy (70.4% [95 CI: 64.8-75.7]). For renal dose–adjustment identification, GPT-4o-mini demonstrated the highest F1 score (83.3% [95 CI: 77.6-88]). Correctness-consistency scores ranged from 8.0% to 97.6% across benchmarks, with no model exhibiting uniformly superior consistency. Conclusions: Model performance varied substantially across medication-related tasks. LLaMA3-70B demonstrated promising baseline performance in tasks involving formulation, ordering, route, and indication. GPT-4o-mini showed potential advantages in drug–drug interaction detection and renal dose adjustment. These findings underscore the need for task-specific evaluation when deploying models for medication-focused clinical decision support.

Background: Differences in total daily energy expenditure (TDEE) across sports, sex, and skill level support the need for sport- and athlete-specific energy intake recommendations. The purpose of the current review was to examine TDEE and related markers of energy expenditure across field-based team sports. A secondary aim was to evaluate physical activity levels (PAL), calculated as TDEE divided by resting metabolic rate (RMR), and their utility in estimating energy needs within team sports. Methods: The review was limited to studies that included the field-based team sports of rugby or soccer and reported energy expenditure data using doubly labeled water (DLW). A literature review identified 11 studies meeting criteria. Weighted means (Xw) and standard deviations (SDw) were calculated for each variable when pooled across each sport category. Results: Rugby (4417 ± 654 kcal·d−1) had a higher average TDEE than soccer (3157 ± 331 kcal/day; p < 0.001). When normalized to body mass, rTDEE was similar between sports (rugby: 49.5 ± 1.3 kcal·kg−1·day−1; soccer: 49.3 ± 1.8 kcal·kg−1·day−1; p = 0.967). PAL values were significantly higher in rugby (2.2 ± 0.4) compared to soccer (1.7 ± 0.2; p = 0.004). RMR was also greater in rugby (2136 ± 322 kcal·d−1) compared to soccer (1835 ± 208 kcal·d−1; p = 0.04). Conclusions: Rugby athletes exhibited higher TDEE values than soccer athletes, reflecting greater absolute energy demands. However, similar relative TDEE values suggest that differences in body size and composition likely contribute to the observed differences in absolute expenditure. These findings underscore the importance of individualized nutrition strategies within team sports and highlight PAL as a useful metric to contextualize energy requirements.