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Record W4403475485 · doi:10.1002/cl2.1444

Protocol: Assessing the impact of interest‐holder engagement on guideline development: A systematic review

2024· review· en· W4403475485 on OpenAlexafffund
Lyubov Lytvyn, Jennifer Petkovic, Joanne Khabsa, Olivia Magwood, Pauline Campbell, Ian D. Graham, Kevin Pottie, Julia Bidonde, Heather Limburg, Danielle Pollock, Elie A. Akl, Thomas W. Concannon, Peter Tugwell

Bibliographic record

VenueCampbell Systematic Reviews · 2024
Typereview
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsOttawa HospitalDalhousie UniversityPublic Health Agency of CanadaBruyèreUniversity of OttawaMcMaster UniversityImpact
FundersNational Health and Medical Research CouncilMedical Research CouncilAgency for Healthcare Research and QualityCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchNational Institute for Health and Care ExcellencePatient-Centered Outcomes Research Institute
KeywordsChecklistGuidelineProtocol (science)Systematic reviewEmpirical researchProcess (computing)PsychologyMedicineMedical educationMEDLINEComputer scienceAlternative medicinePolitical scienceMathematicsPathology

Abstract

fetched live from OpenAlex

This is the protocol for a Campbell systematic review. The objectives are as follows. The objective of this review is to identify and synthesize empirical research on the impacts of interest-holder engagement on the guideline development process and content. Our research questions are as follows: (1) What are the empirical examples of impact on the process in health guideline development across any of the 18 steps of the GIN-McMaster checklist? (2) What are the empirical examples of impact on the content in health guideline development across any of the 18 steps of the GIN-McMaster checklist?

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 imitation

Not 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.

metaresearch head score (Codex)0.178
metaresearch head score (Gemma)0.319
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.178
Threshold uncertainty score0.942

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1780.319
Meta-epidemiology (narrow)0.0050.007
Meta-epidemiology (broad)0.0140.013
Bibliometrics0.0120.015
Science and technology studies0.0050.007
Scholarly communication0.0110.011
Open science0.0050.007
Research integrity0.0110.013
Insufficient payload (model declined to judge)0.1460.033

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.

Opus teacher head0.759
GPT teacher head0.653
Teacher spread0.106 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreProtocol

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".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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