Bibliographic record
Abstract
Background: Systematic reviews (SRs) have become increasingly popular to a wide range of stakeholders.We set out to capture a representative cross-sectional sample of published SRs and examine them in terms of a broad range of epidemiological, descriptive, and reporting characteristics, including emerging aspects not previously examined.Methods and findings: We searched Medline for SRs indexed during November 2004 and written in English.Citations were screened and those meeting our inclusion criteria were retained.Data were collected using a 51-item data collection form designed to assess the epidemiological and reporting details and the bias-related aspects of the reviews.The data were analyzed descriptively.In total 300 SRs were identified, suggesting a current annual publication rate of about 2500, involving more than 33 700 separate studies, including one-third of a million participants.The majority (272 (90.7%)) of SRs were reported in specialty journals.Most reviews (213 (71.0%)) were categorized as therapeutic and included a median of 16 studies involving 1112 participants.Funding sources were not reported in more than onethird (122 (40.7%)) of the reviews.Reviews typically searched a median of three electronic databases and two other sources, although only about two-thirds (208 (69.3%)) of them reported the years searched.Most (197/295 (66.8%)) reviews reported information about quality assessment, while few (68/294 (23.1%)) reported assessing for publication bias.A little over half (161/300 (53.7%)) of the SRs reported combining their results statistically, of which most (147/161 (91.3%)) assessed for consistency across studies.Few (53 (17.7%))SRs reported being updates of previously completed reviews.No review had a registration number.Only half (150 (50.0%)) of the reviews used the term "systematic review" or "meta-analysis" in the title or abstract.There were large differences between Cochrane reviews and non-Cochrane reviews in the quality of reporting several characteristics.Conclusions: SRs are now produced in large numbers, and our data suggest that the quality of their reporting is inconsistent.This situation might be improved if more widely agreed upon evidence-based reporting guidelines were endorsed and adhered to by authors and journals.These results substantiate the view that readers should not accept SRs uncritically.
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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.253 | 0.131 |
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".