Epidemiology and reporting characteristics of overviews of reviews of healthcare interventions published 2012–2016: protocol for a systematic review
Bibliographic record
Abstract
BACKGROUND: Overviews of systematic reviews (overviews) attempt to systematically retrieve and summarize the results of multiple systematic reviews (SRs) for a given condition or public health problem. Two prior descriptive analyses of overviews found substantial variation in the methodological approaches used in overviews, and deficiencies in reporting of key methodological steps. Since then, new methods have been developed so it is timely to update the prior descriptive analyses. The objectives are to: (1) investigate the epidemiological, descriptive, and reporting characteristics of a random sample of 100 overviews published from 2012 to 2016 and (2) compare these recently published overviews (2012-2016) to those published prior to 2012 (based on the prior descriptive analyses). METHODS: Medline, EMBASE, and CDSR will be searched for overviews published 2012-2016, using a validated search filter for overviews. Only overviews written in English will be included. All titles and abstracts will be screened by one review author; those deemed not relevant will be verified by a second person for exclusion. Full-texts will be assessed for inclusion by two reviewers independently. Of those deemed relevant, a random sample of 100 overviews will be selected for inclusion. Data extraction will be either performed by one reviewer with verification by a second reviewer or by one reviewer only depending on the complexity of the item. Discrepancies at any stage will be resolved by consensus or consulting a third person. Data will be extracted on the epidemiological, descriptive, and reporting characteristics of each overview. Data will be analyzed descriptively. When data are available for both time points (up to 2011 vs. 2012-2016), we will compare characteristics by calculating risk ratios or applying the Mann-Whitney test. DISCUSSION: Overviews are becoming increasingly valuable evidence syntheses, and the number of published overviews is increasing. However, former analyses found limitations in the conduct and reporting of overviews. This update of a recent sample of overviews will inform whether this has changed, while also identifying areas for further improvement. SYSTEMATIC REVIEW REGISTRATION: The review will not be registered in PROSPERO as it does not meet the eligibility criterion of dealing with health-related outcomes.
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 | 0.272 | 0.416 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.018 | 0.020 |
| Bibliometrics | 0.027 | 0.028 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.052 | 0.016 |
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".