Systematic reviews: a cross-sectional study of location and citation counts
Bibliographic record
Abstract
BACKGROUND: Systematic reviews summarize all pertinent evidence on a defined health question. They help clinical scientists to direct their research and clinicians to keep updated. Our objective was to determine the extent to which systematic reviews are clustered in a large collection of clinical journals and whether review type (narrative or systematic) affects citation counts. METHODS: We used hand searches of 170 clinical journals in the fields of general internal medicine, primary medical care, nursing, and mental health to identify review articles (year 2000). We defined 'review' as any full text article that was bannered as a review, overview, or meta-analysis in the title or in a section heading, or that indicated in the text that the intention of the authors was to review or summarize the literature on a particular topic. We obtained citation counts for review articles in the five journals that published the most systematic reviews. RESULTS: 11% of the journals concentrated 80% of all systematic reviews. Impact factors were weakly correlated with the publication of systematic reviews (R2 = 0.075, P = 0.0035). There were more citations for systematic reviews (median 26.5, IQR 12 - 56.5) than for narrative reviews (8, 20, P <.0001 for the difference). Systematic reviews had twice as many citations as narrative reviews published in the same journal (95% confidence interval 1.5 - 2.7). CONCLUSIONS: A few clinical journals published most systematic reviews. Authors cited systematic reviews more often than narrative reviews, an indirect endorsement of the 'hierarchy of evidence'.
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 | 0.052 | 0.307 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.025 | 0.048 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".