The Validity of Level of Evidence Ratings of Articles Submitted to JBJS
Bibliographic record
Abstract
BACKGROUND: In 2003, The Journal of Bone & Joint Surgery (American Volume) implemented a requirement for submitted clinical research articles to include a level of evidence rating. The aim of this study was to analyze the agreement between authors and JBJS regarding the level of evidence rating of accepted clinical articles. METHODS: A random sample of 353 clinical research articles accepted by JBJS from 2010 to 2012 was analyzed; 188 had a level of evidence rating provided by the author. Articles were grouped by study type and subspecialty. An unweighted kappa value was calculated to measure agreement between the authors and the JBJS editor, whose decision was used as the gold standard. In a secondary analysis, the articles in each subspecialty were categorized according to the year of submission to evaluate temporal trends. RESULTS: Of the 353 articles, 69.4% (245) were classified by JBJS as representing a therapeutic study, 17.6% (sixty-two) were classified as representing Level-I evidence, and 25.2% (eighty-nine) dealt with arthroplasty. Agreement between the author and the JBJS editor was 0.79 (95% confidence interval [CI], 0.71 to 0.89; p < 0.001) for the study type, 0.62 (95% CI, 0.53 to 0.70; p < 0.001) for the level of evidence, and 0.65 (95% CI, 0.58 to 0.73; p < 0.001) for the full level of evidence rating (study type and level of evidence). CONCLUSIONS: Level of evidence ratings suggested by authors from 2010 to 2012 had moderate to substantial agreement with the ratings assigned by the JBJS editor. This suggests that the level of evidence rating system is being properly understood by authors of articles published in JBJS. However, the low frequency of reporting suggests that JBJS needs to strictly enforce requirements for submission of a level of evidence rating at the time of manuscript submission.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.311 | 0.747 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.036 | 0.016 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".