Factors associated with citation rates in the orthopedic literature.
Bibliographic record
Abstract
INTRODUCTION: Investigators aim to publish their work in top journals in an effort to achieve the greatest possible impact. One measure of impact is the number of times a paper is cited after its publication in a journal. We conducted a review of the highest impact clinical orthopedic journal (Journal of Bone and Joint Surgery, American volume [J Bone Joint Surg Am]) to determine factors associated with subsequent citations within 3 years of publication. METHODS: We conducted citation counts for all original articles published in J Bone Joint Surg Am 2000 (12 issues). We used regression analysis to identify factors associated with citation counts. RESULTS: We identified 137 original articles in the J Bone Joint Surg Am. There were 749 subsequent citations within 3 years of publication of these articles. Study design was the only variable associated with subsequent citation rate. Meta-analyses, randomized trials and basic science papers received significantly more citations (mean 15.5, 9.3 and 7.6, respectively) than did observational studies (mean retrospective 5.3, prospective 4.2) and case reports (mean 1.5) (p = 0.01). These study designs were also significantly more likely to be cited in the general medical literature (p = 0.02). CONCLUSION: Our results suggest that basic science articles and clinical articles with greater methodological safeguards against bias (randomized controlled trials and meta-analyses) are cited more frequently than are clinical studies with less rigorous study designs (observational studies and case reports).
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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 | 0.038 | 0.385 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.021 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".