While large language models (LLMs) have shown promise as learning tools for medical education, their reported accuracy on multiple-choice questions (MCQs) varies widely across studies, necessitating synthesis. This meta-analysis synthesizes LLM accuracy on text-based MCQs from biomedical disciplines and USMLE Step 1-level content and explores study characteristics that moderate LLM performance. Studies published between January 1, 2022, and August 5, 2025, were identified using seven databases. Titles and abstracts were screened against eligibility criteria, which included testing LLM performance on text-based MCQs in English. Extracted data included accuracy rates (correct responses/total questions) and study characteristics. A random-effects proportional meta-analysis was used to calculate the pooled accuracy of LLM versions across studies, and a meta-regression tested the effects of study characteristics on accuracy. The final analysis included 41 articles comprising 207 proportions. Newer OpenAI models (GPT-4o through GPT o1 = 90%; GPT-4 = 82%) demonstrated significantly higher accuracy than earlier versions (GPT-3/3.5 = 59%; p < 0.001). Model version explained 56% of the total variance (p < 0.001). The pooled accuracy of newer Anthropic (Claude 3 through 3.7 = 86%) and DeepSeek (R1 = 86%) versions fell within the range of performance for the newer OpenAI versions, while newer Google (Gemini series = 76%) and Microsoft (Copilot = 69%) versions demonstrated lower performance. A discipline-level analysis demonstrated that OpenAI's GPT-4 through GPT-o1 models achieved pooled accuracy rates between 83 and 87% for most disciplines, including the anatomical sciences. These findings demonstrate that newer LLMs are achieving higher accuracy than older models on biomedical and Step 1-level MCQs, supporting their potential integration as supplementary study tools in foundational biomedical education.
This article examines the editorial policies and structural characteristics of Global South-based communication, media, and journalism academic journals, analyzing how they balance the tension between seeking international recognition and addressing the needs of local audiences. Using two datasets-one detailing journal attributes and another mapping editorial board members-we investigated the websites of 71 SCImago Journal & Country Rank-indexed journals published across 27 non-Western countries. Findings reveal a prevailing adherence to Global North academic standards, which might constrain de-Westernization efforts and South-South collaboration. The strategies and challenges these outlets face underscore persistent inequalities in global knowledge production and visibility.
Similarity search on image embeddings is a common practice for image retrieval in machine learning and pattern recognition systems. Approximate nearest neighbor (ANN) methods enable scalable similarity search on large datasets, often approaching sub-linear complexity. Yet, little empirical work has examined how ANN neighborhood geometry differs from that of exact k-nearest neighbors (k-NN) search as the neighborhood size increases under constrained search effort. This study quantifies how approximate neighborhood structure changes relative to exact k-NN search as k increases across three experimental conditions. Using multiple random subsets of 10,000 images drawn from the STL-10 dataset, we compute ResNet-50 image embeddings, perform an exact k-NN search, and compare it to a Hierarchical Navigable Small World (HNSW)-based ANN search under controlled hyperparameter regimes. We evaluated the fidelity of neighborhood structure using neighborhood overlap, average neighbor distance, normalized barycenter shift, and local intrinsic dimensionality (LID). Results show that exact k-NN and ANN search behave nearly identically when efSearch>k. However, as the neighborhood size grows and efSearch remains fixed, ANN search fails abruptly, exhibiting extreme divergence in neighbor distances at approximately k≈2–3.5×efSearch. Increasing index construction quality delays this failure, and scaling search effort proportionally with neighborhood size (efSearch=α×k with α≥1) preserves neighborhood geometry across all evaluated metrics, including LID. The findings indicate that ANN search preserves neighborhood geometry within its operational capacity but abruptly fails when this capacity is exceeded. Documenting this behavior is relevant for scientific applications that approximate embedding spaces and provides practical guidance on when ANN search is interchangeable with exact k-NN and when geometric differences become nontrivial.
The turnover of senior-level local government employees has been linked to negative organizational outcomes, including increased costs, decreased productivity, and diminished customer experiences. Researchers sought to determine the extent to which Public Service Motivation (PSM) and Psychological Capital (PsyCap) can mitigate this turnover. Results show that PSM accounts for only 2.24% of variance in turnover intentions, while PsyCap accounts for 22.11%. Stepwise regression evaluating individual PSM and PsyCap components yielded a model (resilience, optimism, hope, and attraction to policy making) that explained 32.8% of variance. This research demonstrates that PSM may be what leads people to want to enter the career field, but PsyCap is the resource needed to keep them from leaving the field. These findings suggest that while PSM may attract individuals to public service, PsyCap provides the psychological resources needed for retention. This research underscores the need for further investigation of PsyCap's role in public sector workforce stability.
Background and Purpose: Pulmonary embolism (PE) is a life-threatening condition that rarely occurs in high altitude (HA) environments. Typically PE does not present as musculoskeletal (MSK) pain. The purpose of this case report is to describe successful physical therapist (PT) management of a patient who presented with low back pain (LBP) during a HA expedition, where further evaluation and imaging identified primary impairment due to PE. Case Description and Intervention: A standardized clinical examination was conducted inside a tea house along the Everest Base Camp trek with limited resources. The patient complained of an intermittent “dull ache” at the left lumbar spine and left costovertebral angle, but the MSK examination was unremarkable. Initial differential diagnosis was acute mountain sickness (AMS) versus kidney stones. Fingertip oxygen saturation (SpO 2 ) was 68% at the PT evaluation but deteriorated to 45% after climbing to higher altitudes. Thus, helicopter evacuation to Kathmandu was coordinated to expedite emergency department evaluation. Outcomes: Radiographs showed left pleural effusion, laboratory results noted elevated D-dimer, and computed tomography (CT) scan revealed segmental bilateral PE. The patient recovered in the hospital for several days before flying home. At 3-month follow-up, the symptoms resolved, and follow-up testing was negative for PE. Discussion: Pulmonary embolism may present as referred LBP in conjunction or independent from AMS. Further research is recommended to investigate the incidence of PE presenting as MSK pain, and clinical signs that could be used as screening criteria to categorize risk for those planning high altitude expeditions.
