Why Do We Need a New Clinical Practice Guideline for Moderate to Severe Traumatic Brain Injury?
Bibliographic record
Abstract
OBJECTIVE: Clinical practice guidelines (CPGs) aim to improve quality and consistency of healthcare services. A Canadian group of researchers, clinicians, and policy makers developed/adapted a CPG for rehabilitation post-moderate to severe traumatic brain injury (MSTBI) to respond to end users' needs in acute care and rehabilitation settings. METHODS: The rigorous CPG development process began assessing needs and expectations of end users, then appraised existing CPGs, and, during a consensus conference, produced fundamental and priority recommendations. We also surveyed end users' perceptions of implementation gaps to determine future implementation strategies to optimize adherence to the CPG. RESULTS: The unique bilingual (French and English) CPG consists of 266 recommendations (of which 126 are new recommendations), addressing top priorities for MSTBI, rationale, process indicators, and implementations tools (eg, algorithms and benchmarks). CONCLUSION: The novel approach of consulting and working with end users to develop a CPG for MSTBI should influence knowledge uptake for clinicians wanting to provide evidence-based care.
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 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 | 0.066 | 0.295 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".