Bibliometric study of ‘overviews of systematic reviews’ of health interventions: evaluation of prevalence, citation and impact factor
Bibliographic record
Abstract
Abstract Background: Overviews synthesising the results of multiple systematic reviews help inform evidence-based clinical practice. In this first of two companion papers, we evaluate the bibliometrics of overviews, including their prevalence and factors affecting citation rates and JIF (JIF).Methods: We searched MEDLINE, Epistemonikos and Cochrane Database of Systematic Reviews (CDSR). We included overviews that: (a) synthesised reviews, (b) conducted a systematic search, (c) had a methods section, and (d) examined a healthcare intervention. Multivariable regression was conducted to determine the association between citation density, JIF and 6 predictor variables. Results: We found 1218 overviews published from 2000 to 2020; the majority (73%) were published in the most recent 5-year period. We extracted a selection of these overviews (n=541; 44%) dated from 2000 to 2018. The 541 overviews were published in 307 journals; CDSR (8%), PLOS ONE (3%) and Sao Paulo Medical Journal (2%) were the most prevalent. The majority (70%) were published in journals with impact factors between 0.05 and 3.97. We found a mean citation count of 10 overviews per year, published in journals with a mean JIF of 4.4. In multivariable analysis, overviews with a high number of citations and JIFs had more authors, larger sample sizes, were open access and reported the funding source. Conclusions: An 8-fold increase in the number of overviews was found between 2009 and 2020. We identified 332 overviews published in 2020, which is equivalent to 1 overview published per day. Overviews perform above average for the journals in which they publish.
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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 | 0.196 | 0.649 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.162 | 0.167 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".