Methodological quality, guidance, and tools in scoping reviews: a scoping review protocol
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
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 METHODS: A three-step search strategy will be used to locate both published and unpublished documents.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 Google and Google Scholar will be searched for difficult-to-locate and unpublished literature.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.
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 | 0.413 | 0.801 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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 it