Use of Multimedia on the Step 1 and Step 2 Clinical Knowledge Components of USMLE: A Controlled Trial of the Impact on Item Characteristics
Bibliographic record
Abstract
BACKGROUND: During 2007, multimedia-based presentations of selected clinical findings were introduced into the United States Medical Licensing Examination. This study investigated the impact of presenting cardiac auscultation findings in multimedia versus text format on item characteristics. METHOD: Content-matched versions of 43 Step 1 and 51 Step 2 Clinical Knowledge (CK) multiple-choice questions describing common pediatric and adult clinical presentations were administered in unscored sections of Step 1 and Step 2 CK. For multimedia versions, examinees used headphones to listen to the heart on a simulated chest while watching video showing associated chest and neck vein movements. Text versions described auscultation findings using standard medical terminology. RESULTS: Analyses of item responses for first-time examinees from U.S./Canadian and international medical schools indicated that multimedia items were significantly more difficult than matched text versions, were less discriminating, and required more testing time. CONCLUSIONS: Examinees can more readily interpret auscultation findings described in text using standard terminology than those same findings presented in a more authentic multimedia format. The impact on examinee performance and item characteristics is substantial.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".