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Record W3090448663 · doi:10.1503/cmaj.200193

Assessing the process and outcome of the development of practice guidelines and recommendations: PANELVIEW instrument development

2020· review· en· W3090448663 on OpenAlexfundvenueno aff
Wojtek Wiercioch, Elie A. Akl, Nancy Santesso, Yuan Zhang, Rebecca L. Morgan, Juan José Yepes-Núñez, Sérgio Cândido Kowalski, Tejan Baldeh, Reem A. Mustafa, Kaja-Triin Laisaar, Ulla Raid, Itziar Etxeandia‐Ikobaltzeta, Alonso Carrasco‐Labra, Matthew Ventresca, Ignacio Neumann, Maicon Falavigna, Romina Brignardello‐Petersen, Gian Paolo Morgano, Jan Brożek, Meghan McConnell, Holger J. Schünemann

Bibliographic record

VenueCanadian Medical Association Journal · 2020
Typereview
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsnot available
FundersEesti TeadusagentuurMedical Research CouncilPublic Health AgencyRobert Koch InstitutKoch Institute for Integrative Cancer Research, Massachusetts Institute of TechnologyNational Institute for Health and Care ExcellenceNational Health and Medical Research CouncilMcMaster UniversityWorld Health Organization
KeywordsGuidelineFace validityProcess (computing)PsychologyReliability (semiconductor)Medical educationApplied psychologyProcess managementComputer scienceMedicineEngineeringClinical psychologyPsychometricsPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Guideline recommendations may be affected by flaws in the process, inappropriate panel member selection or conduct, conflicts of interest and other factors. To our knowledge, no validated tool exists to evaluate guideline development from the perspective of those directly involved in the process. Our objective was to develop and validate a universal tool, the PANELVIEW instrument, to assess guideline processes, methods and outcomes from the perspective of the participating guideline panellists and group members. METHODS: We performed a systematic literature search and surveys of guideline groups (identified through contacting international organizations and convenience sampling of working panels) to inform item generation. Subsequent groups of guideline methodologists and panellists reviewed items for face validity and missing items. We used surveys, interviews and expert review for item reduction and phrasing. For reliability assessment and feedback, we tested the PANELVIEW tool in 8 international guideline groups. RESULTS: We surveyed 62 members from 13 guideline panels, contacted 19 organizations and reviewed 20 source documents to generate items. Fifty-three additional key informants provided feedback about phrasing of the items and response options. We reduced the number of items from 95 to 34 across domains that included administration, training, conflict of interest, group dynamics, chairing, evidence synthesis, formulating recommendations and publication. The tool takes about 10 minutes to complete and showed acceptable measurement properties. INTERPRETATION: The PANELVIEW instrument fills a gap by enabling guideline organizations to involve clinicians, patients and other participants in evaluating their guideline processes. The tool can inform quality improvement of existing or new guideline programs, focusing on insight into and transparency of the guideline development process, methods and 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 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)0.229
metaresearch head score (Gemma)0.366
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.771
Threshold uncertainty score0.951

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2290.366
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0090.007
Science and technology studies0.0010.003
Scholarly communication0.0050.006
Open science0.0020.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.002

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 head0.358
GPT teacher head0.549
Teacher spread0.192 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreReview

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

Citations37
Published2020
Admission routes2
Has abstractyes

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