What is taught, what is tested: Findings and competency-based recommendations of the Undergraduate Medical Education Committee of the Society of Critical Care Medicine
Bibliographic record
Abstract
INTRODUCTION: Addressing an unexpected shortfall of intensivists requires early identification and training of appropriate personnel. The purpose of this study was to determine how U.S. medical students are currently educated and tested on acute care health principles. HYPOTHESIS/METHODS: A survey of critical care education with telephone follow-up was mailed to the deans of all 126 medical schools. Web site review of medical school curricula for critical care education was performed. Upon invited request, four members of the Undergraduate Medical Education Committee (UGMEC) reviewed 1,200 pool questions of step II of the U.S. Medical Licensing Examination (USMLE) given to graduating medical students for critical care content. Descriptive statistics are employed. RESULTS: Survey response rate was 49% and 88% by the second mailing with Web site review. Forty-five percent of U.S. medical schools responding had formal undergraduate critical care didactic curricula averaging 12 +/- 3 hrs: 60% were elective, 60% taught in the 4th year. Eighty percent of clinical ICU rotations offered were elective. Sixty percent of schools taught 11 key critical care procedures in the 3rd or 4th year; 17% required them to graduate. Nineteen percent of Step II USMLE questions had critical care content; 58% dealt with pulmonary or cardiac disease. CONCLUSIONS: Graduating medical students are tested (and licensed accordingly) on critical care knowledge, despite an inconsistent exposure to the discipline in medical school. The UGMEC has drafted competency-based recommendations for acute health care delivery that encourage mandatory didactic and procedural critical care training. The UGMEC recommends that critical care rotations with didactic curricula be required for undergraduate education and that acute care procedural skills be an important component of these curricula.
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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 | 0.072 | 0.172 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".