Recent meta-analyses neglect previous systematic reviews and meta-analyses about the same topic: a systematic examination
Bibliographic record
Abstract
BACKGROUND: As the number of systematic reviews is growing rapidly, we systematically investigate whether meta-analyses published in leading medical journals present an outline of available evidence by referring to previous meta-analyses and systematic reviews. METHODS: We searched PubMed for recent meta-analyses of pharmacological treatments published in high impact factor journals. Previous systematic reviews and meta-analyses were identified with electronic searches of keywords and by searching reference sections. We analyzed the number of meta-analyses and systematic reviews that were cited, described and discussed in each recent meta-analysis. Moreover, we investigated publication characteristics that potentially influence the referencing practices. RESULTS: We identified 52 recent meta-analyses and 242 previous meta-analyses on the same topics. Of these, 66% of identified previous meta-analyses were cited, 36% described, and only 20% discussed by recent meta-analyses. The probability of citing a previous meta-analysis was positively associated with its publication in a journal with a higher impact factor (odds ratio, 1.49; 95% confidence interval, 1.06 to 2.10) and more recent publication year (odds ratio, 1.19; 95% confidence interval 1.03 to 1.37). Additionally, the probability of a previous study being described by the recent meta-analysis was inversely associated with the concordance of results (odds ratio, 0.38; 95% confidence interval, 0.17 to 0.88), and the probability of being discussed was increased for previous studies that employed meta-analytic methods (odds ratio, 32.36; 95% confidence interval, 2.00 to 522.85). CONCLUSIONS: Meta-analyses on pharmacological treatments do not consistently refer to and discuss findings of previous meta-analyses on the same topic. Such neglect can lead to research waste and be confusing for readers. Journals should make the discussion of related meta-analyses mandatory.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.335 | 0.411 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.029 | 0.006 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; both teacher heads 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".