The US Residency Selection Process After the United States Medical Licensing Examination Step 1 Pass/Fail Change: Overview for Applicants and Educators
Bibliographic record
Abstract
The United States Medical Licensing Examination (USMLE) Step 1, arguably the most significant assessment in the USMLE examination series, changed from a 3-digit score to a pass/fail outcome in January 2022. Given the rapidly evolving body of literature on this subject, this paper aims to provide a comprehensive review of the historical context and impact of this change on various stakeholders involved in residency selection. For this, relevant keyword-based searches were performed in PubMed, Google Scholar, and Scopus to identify relevant literature. Given the unique history of USMLE Step 1 in the US residency selection process and the score's correlation with future performance in board-certifying examinations in different specialties, this scoring change is predicted to significantly impact US Doctor of Medicine students, US Doctor of Osteopathic Medicine students, international medical graduates, and residency program directors, among others. The significance and the rationale of the pass/fail change along with the implications for both residency applicants and educators are also summarized in this paper. Although medical programs, academic institutions, and residency organizing bodies across the United States have swiftly stepped up to ensure a seamless transition and have attempted to ensure equity for all, the conversion process carries considerable uncertainty for residency applicants. For educators, the increasing number of applications conflicts with holistic application screening, leading to the expected greater use of objective measures, with USMLE Step 2 Clinical Knowledge likely becoming the preferred screening tool in lieu of Step 1.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".