Lessons Learned From Moving to Living Guidelines—The Canadian Clinical Practice Guideline for the Rehabilitation of Adults With Moderate-to-Severe TBI
Bibliographic record
Abstract
OBJECTIVE: It is often challenging for providers to remain up to date with best practices gleaned from clinical research. Consequently, patients may receive inappropriate, suboptimal, and costly care. Living clinical practice guidelines (CPGs) maintain the methodological rigor of traditional CPGs but are continuously updated in response to new research findings, changes in clinical practice, and emerging evidence. The objective of this initiative was to discuss the lessons learned from the transformation of the Canadian Clinical Practice Guideline for the Rehabilitation of Adults with Traumatic Brain Injury (CAN-TBI) from a traditional guideline update model to a living guideline model. DESIGN: The CAN-TBI Guideline provides evidence-based rehabilitative care recommendations for individuals who have sustained a TBI. The Guideline is divided into 2 sections: Section I, which provides guidance on the components of the optimal TBI rehabilitation system, and Section II, which focuses on the assessment and rehabilitation of brain injury sequelae. A comprehensive outline of the living guideline process is presented. RESULTS: The CAN-TBI living guideline process has yielded 351 recommendations organized within 21 domains. Currently, 30 recommendations are supported by level A evidence, 81 recommendations are supported by level B evidence, and 240 consensus-based recommendations (level C evidence) comprise 68% of the CAN-TBI Guideline. CONCLUSION: Given the increasing volume of research published on moderate-to-severe TBI rehabilitation, the CAN-TBI living guideline process allows for real-time integration of emerging evidence in response to the fastest-growing topics, ensuring that practitioners have access to the most current and relevant recommendations.
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 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 | 0.018 | 0.226 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; a candidate call from one teacher head, not a consensus.
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".