The United States Medical Licensing Exam Step 2 Clinical Skills Examination: Potential Alternatives During and After the COVID-19 Pandemic
Bibliographic record
Abstract
We feel that the current COVID-19 crisis has created great uncertainty and anxiety among medical students. With medical school classes initially being conducted on the web and the approaching season of "the Match" (a uniform system by which residency candidates and residency programs in the United States simultaneously "match" with the aid of a computer algorithm to fill first-year and second-year postgraduate training positions accredited by the Accreditation Council for Graduate Medical Education), the situation did not seem to be improving. The National Resident Matching Program made an official announcement on May 26, 2020, that candidates would not be required to take or pass the United States Medical Licensing Examination Step 2 Clinical Skills (CS) examination to participate in the Match. On January 26, 2021, formal discontinuation of Step 2 CS was announced; for this reason, we have provided our perspective of possible alternative solutions to the Step 2 CS examination. A successful alternative model can be implemented in future residency match seasons as well.
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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 | 0.031 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".