Epidemiology and reporting characteristics of <scp>non‐Cochrane</scp> updates of systematic reviews: A cross‐sectional study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.809 | 0.972 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it