Retention of Basic Science Information by Senior Medical Students
Bibliographic record
Abstract
BACKGROUND: Studies of retention of basic science information have commonly demonstrated a knowledge decline as students progress through medical education. This study examined item characteristics influencing patterns of retention. METHOD: A large content and statistically representative sample of basic science items from 2004-2005 forms of United States Medical Licensing Examination (USMLE) Step 1 was included in unscored sections of 2004-2005 USMLE Step 2 Clinical Knowledge (CK) test forms, and the performance of 15,000+ first-time examinees from U.S. and Canadian schools was analyzed to identify item characteristics affecting retention. RESULTS: Across the 502 study items, the mean item difficulty on Step 1 was 76.1%; on Step 2 CK, this value declined to 69.7%. Performance declines were largest in Biochemistry (17.5%) and Microbiology (12.6%). Improvement was only observed for Behavioral Sciences items (8.7%). CONCLUSIONS: Shifts in examinee performance in this study were similar to those observed in previous research, although the magnitude of the overall decline was somewhat larger.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".