Doubling the Impact
Bibliographic record
Abstract
BACKGROUND: Investigators aim to publish their research papers in top journals to disseminate their findings to the widest possible audience. Systematic reviews of the literature occupy the highest position in currently proposed hierarchies of evidence. We hypothesized that the number of citations (a measure of scholarly interest) for systematic reviews (or meta-analyses) published in leading orthopaedic journals would be greater than the number of citations for narrative reviews published in the same journals. METHODS: We identified fifteen journals that had high Science Citation Index impact factors for the orthopaedic subspecialty and were believed to have a higher yield of studies and reviews of scientific merit and clinical relevance. For the year 2000, six research associates applied methodological criteria to each article in each issue of the fifteen journals to determine whether the article was scientifically sound (rigorous versus nonrigorous). Of the 3916 articles identified, 2331 were original or review articles. We queried the ISI (Institute for Scientific Information) Web of Science database to ascertain, as of March 2003, the number of subsequent citations to each one of the reviews after its original publication in all journals that published both narrative and systematic reviews. RESULTS: Of the 2331 articles published across the fifteen journals in the year 2000, 110 were review articles. Only seventeen (15%) of the 110 reviews met our criteria for systematic reviews with rigor. Rigorous systematic reviews received more than twice the mean number of citations compared with other systematic or narrative reviews (13.8 compared with 6.0, p = 0.008). The rigor of a review was a significant predictor of the number of citations in other orthopaedic journals (p = 0.01). In addition, rigor was significantly associated with the number of citations in nonorthopaedic journals (p = 0.03). CONCLUSIONS: Our findings suggest that journal editors and authors can improve the relevance and scholarly interest in their reviews (as shown by the number of citations) by meeting standard guidelines for methodological rigor.
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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 | 0.023 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.025 | 0.027 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.112 | 0.054 |
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".