Conducting pairwise and network meta-analyses in updated and living systematic reviews: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to describe existing guidance documents or studies reporting on the conduct of meta-analyses in updated systematic reviews (USRs) or living systematic reviews (LSRs). INTRODUCTION: The rapid increase in the medical literature poses a substantial challenge in keeping systematic reviews up to date. In LSRs, a review is updated with a pre-specified frequency or when some other signalling criterion is triggered. While the LSR framework is well-established, there is uncertainty regarding the most appropriate methods for conducting repeated meta-analyses over time, which may result in sub-optimal decision-making. INCLUSION CRITERIA: Studies of any design (including commentaries, books, manuals) providing guidance on conducting meta-analysis in USRs or LSRs. METHODS: This review will use the JBI methodology for scoping reviews. We will search multiple medical bibliographic databases (Cochrane Library, Embase, ERIC, MEDLINE, JBI Evidence Synthesis , and PsycINFO), statistical and mathematics databases (COBRA, Current Index to Statistics, MathSciNet, Project Euclid Complete, and zbMATH), pre print archives (Arvix, BioRxiv, and MedRxiv), and unpublished (or gray) literature sources. Two reviewers will independently screen titles, abstracts, and full-text documents, and extract data. Characteristics of recommendations for meta-analysis in USRs and LSRs will be presented using descriptive statistics and categorized concepts. REVIEW REGISTRATION: Open Science Framework https://osf.io/9c27g.
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 | 0.209 | 0.236 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.019 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.099 | 0.033 |
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".