Bibliographic record
Abstract
We thank Dr. Schattner for his comments on our article and share his concern regarding the unintended negative consequences that may follow on the discontinuation of the United States Medical Licensing Examination (USMLE) Step 2 Clinical Skills (CS). We maintain that these terrible outcomes are not inevitable. An explicit requirement from the Liaison Committee on Medical Education (LCME) and medical licensing bodies that medical schools conduct rigorous summative assessments of their students’ clinical skills and present robust validity evidence to support pass/fail decisions may mitigate the loss of the Step 2 CS by motivating faculty, students, and administrators to continue to focus on clinical skills. The Association of American Medical Colleges Group on Educational Affairs (GEA) is already mobilizing faculty across the United States and Canada to develop national standards and resources to assist in this effort. With a strong mandate in place, the need to conduct defensible high-stakes assessments locally would ideally lead to an increase in the resources medical schools dedicate to clinical skills assessment. The Federation of State Medical Boards and NBME invested substantial time and effort to mount Step 2 CS as a defensible high-stakes exam. Medical schools can no longer defer this task—and these costs—to the USMLE. School administrators need to be made aware, via the requirements of the LCME and licensing bodies, that replicating the rigor of the Step 2 CS cannot be done without a similar investment of resources at the local level. We hope that the accrediting and licensing bodies will take seriously their responsibility to protect the public by reinforcing the importance of clinical skills instruction and assessment in medical schools. We agree that ongoing teaching and careful assessment of our learners’ clinical skills are essential for promoting patient-centered care. This is a time when the medical education community can come together to cooperatively design and test models for local implementation. We remain optimistic that with the necessary resources our faculty can enhance the assessment of clinical skills with creativity and rigor.
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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 | 0.009 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.033 | 0.060 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".