Designing, Conducting, and Evaluating Journal Clubs in Orthopaedic Surgery
Bibliographic record
Abstract
The first record of a journal club was that founded in 1875 by Sir William Osler at McGill University for the purchase and distribution of periodicals to which he could not afford to subscribe as an individual. Evidence-based medicine is becoming an accepted educational paradigm in medical education at various levels. An analysis of the literature related to journal clubs in residency programs in specialties other than orthopaedic surgery reveals that the three most common goals were to teach critical appraisal skills (67%), to have an impact on clinical practice (59%), and to keep up with the current literature (56%). The implementation of the structured article review checklist has been found to increase resident satisfaction and improves the perceived educational value of the journal club without increasing resident workload or decreasing attendance at the conference. Periodic evaluation of the conference and the institution of appropriate changes ensures that the journal club remains a valuable and successful part of the training program.
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 | 0.334 | 0.552 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".