Characteristics Associated with Citation Rate of the Medical Literature
Bibliographic record
Abstract
BACKGROUND: The citation rate for articles is viewed as a measure of their importance and impact; however, little is known about what features of articles are associated with higher citation rate. METHODOLOGY/PRINCIPAL FINDINGS: We conducted a cohort study of all original articles, regardless of study methodology, published in the Lancet, JAMA, and New England Journal of Medicine, from October 1, 1999 to March 31, 2000. We identified 328 articles. Two blinded, independent reviewers extracted, in duplicate, nine variables from each article, which were analyzed in both univariable and multivariable linear least-squares regression models for their association with the annual rate of citations received by the article since publication. A two-way interaction between industry funding and an industry-favoring result was tested and found to be significant (p = 0.02). In our adjusted analysis, the presence of industry funding and an industry-favoring result was associated with an increase in annual citation rate of 25.7 (95% confidence interval, 8.5 to 42.8) compared to the absence of both industry funding and industry-favoring results. Higher annual rates of citation were also associated with articles dealing with cardiovascular medicine (13.3 more; 95% confidence interval, 3.9 to 22.3) and oncology (12.6 more; 95% confidence interval, 1.2 to 24.0), articles with group authorship (11.1 more; 95% confidence interval, 2.7 to 19.5), larger sample size and journal of publication. CONCLUSIONS/SIGNIFICANCE: Large trials, with group authorship, industry-funded, with industry-favoring results, in oncology or cardiology were associated with greater subsequent citations.
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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 | 0.022 | 0.297 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".