Modeling Passing Rates on a Computer‐Based Medical Licensing Examination: An Application of Survival Data Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this article was to model United States Medical Licensing Examination (USMLE) Step 2 passing rates using the Cox Proportional Hazards Model, best known for its application in analyzing clinical trial data. The number of months it took to pass the computer‐based Step 2 examination was treated as the dependent variable in the model. Covariates in the model were: (a) medical school location (U.S. and Canadian or other), (b) primary language (English or other), and (c) gender. Preliminary findings indicate that examinees were nearly 2.7 times more likely to experience the event (pass Step 2) if they were U.S. or Canadian trained. Examinees with English as their primary language were 2.1 times more likely to pass Step 2, but gender had little impact. These findings are discussed more fully in light of past research and broader potential applications of survival analysis in educational measurement.
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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 | 0.003 | 0.009 |
| 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 it