Methodological quality, guidance, and tools in scoping reviews: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to identify and report on evidence (such as guidance) or tools regarding methodological quality or risk of bias of scoping reviews. INTRODUCTION: Scoping reviews have gained popularity in recent years but have been criticized for variations in their approaches. This scoping review will examine evidence on the methodological quality of scoping reviews. It will also identify and describe potential methods to inform the development of a tool for appraising the methodological quality of scoping reviews. INCLUSION CRITERIA: This review will consider all documents reporting on the development, evaluation, or use of tools addressing the critical appraisal or risk of bias of scoping reviews. The search will seek evidence published from 2005 onwards, corresponding with the publication of Arksey and O'Malley's framework for scoping reviews. METHODS: A three-step search strategy will be used to locate both published and unpublished documents. An initial search of MEDLINE identified keywords and MeSH terms. A second search of MEDLINE, Embase, and CINAHL will follow. Google and Google Scholar will be searched for difficult-to-locate and unpublished literature. The authors will use their professional networks, social media accounts, and professional newsletters to contact methodologists to obtain any additional materials. Documents will be independently screened, selected, and extracted by two researchers, and the data will be presented in tables.
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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 | 0.348 | 0.332 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.065 | 0.030 |
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".