MétaCan
Menu
Back to cohort
Record W4410061662 · doi:10.1186/s12938-025-01385-6

From gaps to guidelines: a process for providing guidance to bridge evidence gaps

2025· article· en· W4410061662 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBioMedical Engineering OnLine · 2025
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsParkwood InstituteWestern UniversityUniversity of TorontoToronto Rehabilitation Institute
FundersToronto Rehabilitation InstituteOntario Ministry of Health and Long-Term Care
KeywordsProcess (computing)Bridge (graph theory)EngineeringComputer scienceProcess managementForensic engineeringRisk analysis (engineering)BusinessMedicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the proliferation of clinical research that can be used to inform Clinical Practice Guidelines there remain many areas where the number and quality of research studies vary widely. Using the Canadian Clinical Practice Guideline for Moderate-to-Severe Traumatic Brain Injury (MOD-SEV TBI) as an example, there is a lack of robust research evidence, derived from randomized controlled trials, meta-analyses, and systematic reviews to inform the recommendations. Randomized controlled trials in this field often have limitations, such as smaller sample sizes and gender and racial disparities in enrollment, that reduce the level of evidence they can provide. Notably, evidence is often lacking in the priority areas identified by people with lived experience (PWLE) and guideline end-users. METHODS: The Canadian Clinical Practice Guideline for MOD-SEV TBI rehabilitation is a Living Guideline that implemented a robust and replicable process to mitigate these issues. This process includes: 1. Identification of Priorities by PWLE of MOD-SEV TBI and Guideline End-Users; 2. Involvement of Diverse Multidisciplinary Expert Panels, Including PWLE; 3. Compilation, Review and Evaluation of Published MOD-SEV TBI Evidence; 4. Identification of Gaps in the Published Literature; 5. Formulation of Recommendations, Rigorous Grading of Available Evidence and Formal Voting; 6. Creation of Knowledge Translation and Mobilization Tools and 7. Publication of the Updated Living Guideline. RESULTS: Since 2014-15, the Canadian TBI Living Guideline has implemented and refined this process to produce high-quality expert consensus-based recommendations and knowledge translation and mobilization tools across 21 comprehensive domains of TBI rehabilitation. There are 351 recommendations in the current version of the Canadian TBI Living Guideline; 68% of these are primarily consensus-based recommendations. Developing a comprehensive guideline in areas where research may not be present or strong ensures that the Guideline is comprehensive and addresses the priority needs of clinicians and PWLE. CONCLUSIONS: The use of robust, transparent, and replicable evidence reviews and expert consensus building process produces clinical guidelines that are relevant and applicable even when empirical data are lacking or absent. This process of developing consensus-based recommendations can be used to develop guidelines in other content areas and populations facing similar challenges.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.078
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.424
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.078
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.265
GPT teacher head0.532
Teacher spread0.268 · 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