Epidemiology and reporting characteristics of <scp>non‐Cochrane</scp> updates of systematic reviews: A cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: It is important that systematic reviews (SRs) are up-to-date, otherwise they cannot be relied upon to guide decision-making in practice and policy. Our aim was to investigate epidemiological, descriptive and reporting characteristics of a cross-section of recently published updates of SRs. METHODS: A SR update was defined as a new edition of a SR, either published by the same or new authors. We searched PubMed for SR updates published from January 01, 2016 to January 22, 2018 and included a random sample of n = 100 non-Cochrane updates of SRs on interventions reported in English. RESULTS: Most SR updates had a corresponding author from the United Kingdom, United States, or Canada (in total 48/100) and dealt with nonpharmacological interventions (63/100). The SR updates were published a median of 5 years (interquartile range [IQR] 3-7) after the previous SR and included a median of 19 (IQR 9-28) studies. 31/100 SR updates reported that the conclusion had changed since the previous version. Only 51/100 SR updates used the term "update" in the title and none reported having based the decision to update the previous SR on an existing method/decision tool. The number of newly included studies and participants and the number of studies and participants included in/from the previous SR were often not reported. CONCLUSIONS: The included non-Cochrane updates were frequently missing important information that would be expected to be present in a SR update. Thus, structured and detailed reporting guidance specific to SR updates is needed. It should focus particularly on appropriate labeling and justification of updates, and how to incorporate information regarding the previous SR.
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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 | 0.631 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.028 | 0.041 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".