Systematic Online Academic Resource (<scp>SOAR</scp>) Review: Renal and Genitourinary
Bibliographic record
Abstract
BACKGROUND: Online resources for emergency medicine (EM) trainees and physicians have variable quality and inconsistent coverage of core topics. In this first entry of the Society for Academic Emergency Medicine Systematic Online Academic Resource (SOAR) series, we describe the application of a systematic methodology to comprehensively identify, collate, and curate online content for topic-specific modules. METHODS: A list of module topics and related terms was generated from the American Board of Emergency Medicine's Model of the Clinical Practice of Emergency Medicine. The authors selected "renal and genitourinary" for the first module, which contained 35 terms; all MeSH headers and colloquial synonyms related to the topic and related terms were searched both within the 100 most impactful online educational websites per the Social Media Index and the FOAMsearch.net search engine. Duplicate entries, journal articles, images, and archives were excluded. The quality of each article was rated using the revised METRIQ (rMETRIQ) score. RESULTS: The search yielded 13,058 online resources. After 12,717 items were excluded, 341 underwent quality assessment. All renal/genitourinary topics were covered by at least one resource. The median rMETRIQ score was 11 of 21 (interquartile range = 8-14). Calculus of urinary tract was most prominently featured with 60 posts. Thirty-four posts (10% of full-text screened FOAM articles) covering 12 core topics were identified as high quality (rMETRIQ ≥ 16). CONCLUSIONS: We demonstrated the feasibility of systematically identifying and curating FOAM resources for a specific EM topic and identified an overrepresentation of some subtopics. This curated list of resources may guide trainees, teacher recommendations, and resource producers. Further entries in the series will address other topics relevant to EM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.025 | 0.037 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.007 |
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 source (direct Gemma or distilled Codex), 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".