Knowledge of Levels of Evidence Criteria in Orthopedic Residents
Bibliographic record
Abstract
The purpose of the levels of evidence system is to provide a framework for critical evaluation of orthopedic literature. This rating system is based on guidelines from the Oxford Centre for Evidence-Based Medicine and is currently in use in several orthopedic surgery journals. The purpose of this study was to investigate resident knowledge of the levels of evidence criteria used in classification of clinical articles. Thirty-eight residents from 5 orthopedic surgery training programs, from year-in-training 3 to 5, determined the levels of evidence rating of 10 blinded articles representing all levels of evidence types in the orthopedic literature. Residents were then provided with a levels of evidence information sheet and asked to re-rate each article. The mean percentage correct for the initial rating was 29.5% and for the post-education rating was 41.3%, with significant improvement after levels of evidence education (P<.001). The year-in-training-3 group had the highest mean percentage correct for the average of both tests (46.7%) compared to year-in-training-4 (34.2%) and year-in-training-5 (25.4%). Residents were significantly more accurate scoring therapeutic (41.1% correct pre-levels of evidence; 51.6% post-levels of evidence) than prognostic studies (6.6% correct pre-levels of evidence; 28.9% post-levels of evidence) (P<.001). Residents graded the level of evidence correctly in fewer than half the papers. These findings indicate that resident knowledge of levels of evidence criteria is limited and suggest a need for more education in this area.
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 | 0.112 | 0.481 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".