An Assessment of USMLE Examinees Found to Have Engaged in Irregular Behavior, 1992–2006
Bibliographic record
Abstract
Purpose: The United States Medical Licensing Examination® (USMLE®) program takes active measures to ensure the integrity of the licensing examination process. This study looks at the examinees found by the USMLE program to have engaged in irregular behavior and their subsequent success in completing the examination sequence and obtaining a full, unrestricted medical license.Methods: Working with the Office of the USMLE Secretariat, all individuals determined by the program to have engaged in irregular behavior related to the examination were identified for the period 1992–2006. These individuals were then searched against databases at the Federation of State Medical Boards for board action history and licensure status.Results: A total of 433 individuals were deemed to have engaged in irregular behavior by the USMLE Committee on Irregular Behavior. Subgroups disproportionately represented included males (66.7%) and international medical graduates (78.8%). Document falsification was the most common infraction under computer-based test administration. Less than half of the irregular behavior cohort (45.7%) successfully completed the USMLE sequence. Only 37.2% completed the USMLE sequence and obtained a full, unrestricted medical license in a U.S. jurisdiction. Graduates of U.S. and Canadian medical schools were the subgroup most likely to complete the USMLE sequence and obtain their medical license.Conclusions: A finding of irregular behavior by the USMLE carries significant potential consequences. State medical boards have denied licenses to individuals with irregular behavior and been unwilling to support the prospective licensure of individuals barred from the program indefinitely.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".