Systematic online academic resource (SOAR) review: Endocrine, metabolic, and nutritional disorders
Bibliographic record
Abstract
BACKGROUND: Free open-access medical education (FOAM) has become an integral resource for medical school and residency education. However, questions of quality and inconsistent coverage of core topics remain. In this second entry of the SAEM Systematic Online Academic Resource (SOAR) series, we describe the application of a systematic methodology to identify, curate, and describe FOAM topics specific to endocrine, metabolic, and nutritional disorders as defined by the 2016 Model of the Clinical Practice of Emergency Medicine (MCPEM). METHODS: We developed an automated algorithm to search 264 keywords derived from nine subtopics within the MCPEM category in the FOAM Search (a customized FOAM search tool) and the Social Media index. The top 100 results were extracted for each keyword. Resources underwent a manual iterative screening process, and those relevant to endocrine, metabolic, or nutritional disorders and EM were evaluated with the revised Medical Education Translational Resources: Impact and Quality (rMETRIQ) tool. RESULTS: < 0.001). A total of 121 posts (16% of posts) covering 25 subtopics were identified as high quality (rMETRIQ ≥16). The most covered subtopic was potassium disorders, representing 15% of all posts. Subtopics that did not have a high-quality resource identified include metabolic alkalosis, respiratory alkalosis, fluid overload, phosphorus metabolism, hyperglycemia, malabsorption, malnutrition, and thyroiditis. From most to least common, the overall target audience was junior resident (91%), PGY-1 resident (88%), senior resident (81%), clerk (64%), attending (50%), and preclerkship (9%). CONCLUSIONS: We systematically identified, described, and curated FOAM resources for EM learners on the topic of endocrine, metabolic, and nutritional disorders. A final list of high-quality resources can guide trainees, educator recommendations, and FOAM authors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".