From gaps to guidelines: a process for providing guidance to bridge evidence gaps
Bibliographic record
Abstract
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.
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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 | 0.400 | 0.675 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.037 | 0.028 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.040 | 0.036 |
| Open science | 0.024 | 0.045 |
| Research integrity | 0.039 | 0.048 |
| Insufficient payload (model declined to judge) | 0.038 | 0.029 |
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".