Updated Canadian Clinical Practice Guideline for the Rehabilitation of Adults With Moderate to Severe Traumatic Brain Injury: Mental Health Recommendations
Bibliographic record
Abstract
OBJECTIVE: Objective: After sustaining a moderate to severe traumatic brain injury (MSTBI), individuals often experience comorbid mental health conditions that can impair the rehabilitation and recovery process. The objective of this initiative was to update recommendations on the assessment and management of mental health conditions for the Canadian Clinical Practice Guideline for the Rehabilitation of Adults with MSTBI (CAN-TBI 2023). OBJECTIVE: Design: A systematic search was conducted by the Evidence-Based Review of Moderate to Severe Acquired Brain Injury to identify new and relevant articles. Expert Panel reviewed and discussed the new and existing evidence, evaluated its quality, and added, removed, or modified recommendations and tools as needed. A consensus process was followed to achieve agreement on recommendations. OBJECTIVE: Results: CAN-TBI 2023 includes 20 recommendations regarding best practices for the assessment and management of mental health conditions post-MSTBI. About 17 recommendations were updated, 1 new recommendation was formed, and 2 recommendations remained unchanged. The Guideline emphasizes the importance of screening and assessment of mental health conditions throughout the rehabilitation continuum. The Expert Panel recommended incorporating multimodal treatments that include pharmacological and nonpharmacological approaches to manage mental health concerns. OBJECTIVE: Conclusion: The CAN-TBI 2023 recommendations for the assessment and management of mental health conditions should be used to inform clinical practice. Additional high-quality research in this area is needed, as 13 recommendations are based on level C evidence, 4 on level B evidence, and 3 on level A evidence.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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 source (direct Gemma or distilled Codex), 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".