Epidemiology and Reporting Characteristics of Systematic Reviews of Biomedical Research: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Systematic reviews (SRs) can help decision makers interpret the deluge of published biomedical literature. However, a SR may be of limited use if the methods used to conduct the SR are flawed, and reporting of the SR is incomplete. To our knowledge, since 2004 there has been no cross-sectional study of the prevalence, focus, and completeness of reporting of SRs across different specialties. Therefore, the aim of our study was to investigate the epidemiological and reporting characteristics of a more recent cross-section of SRs. METHODS AND FINDINGS: We searched MEDLINE to identify potentially eligible SRs indexed during the month of February 2014. Citations were screened using prespecified eligibility criteria. Epidemiological and reporting characteristics of a random sample of 300 SRs were extracted by one reviewer, with a 10% sample extracted in duplicate. We compared characteristics of Cochrane versus non-Cochrane reviews, and the 2014 sample of SRs versus a 2004 sample of SRs. We identified 682 SRs, suggesting that more than 8,000 SRs are being indexed in MEDLINE annually, corresponding to a 3-fold increase over the last decade. The majority of SRs addressed a therapeutic question and were conducted by authors based in China, the UK, or the US; they included a median of 15 studies involving 2,072 participants. Meta-analysis was performed in 63% of SRs, mostly using standard pairwise methods. Study risk of bias/quality assessment was performed in 70% of SRs but was rarely incorporated into the analysis (16%). Few SRs (7%) searched sources of unpublished data, and the risk of publication bias was considered in less than half of SRs. Reporting quality was highly variable; at least a third of SRs did not report use of a SR protocol, eligibility criteria relating to publication status, years of coverage of the search, a full Boolean search logic for at least one database, methods for data extraction, methods for study risk of bias assessment, a primary outcome, an abstract conclusion that incorporated study limitations, or the funding source of the SR. Cochrane SRs, which accounted for 15% of the sample, had more complete reporting than all other types of SRs. Reporting has generally improved since 2004, but remains suboptimal for many characteristics. CONCLUSIONS: An increasing number of SRs are being published, and many are poorly conducted and reported. Strategies are needed to help reduce this avoidable waste in research.
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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 | 0.394 | 0.733 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.026 | 0.040 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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; 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".