Quality of clinical practice guidelines for persons who have sustained mild traumatic brain injury
Bibliographic record
Abstract
BACKGROUND: Mild TBI is one of the most common neurological disorders occurring today. For individuals who experience persistent symptoms following mild TBI, consequences can include functional disability, stress and time away from one's occupation. The objective of the study was to evaluate the quality of clinical practice guidelines (CPGs) that include recommendations on the care of persons who have sustained mild TBI and associated persistent symptoms. METHODS: A minimum of four appraisers used the Appraisal of Guidelines for Research and Evaluation (AGREE) instrument to evaluate seven CPGs found via a systematic search of bibliographic databases and internet resources. RESULTS: High AGREE scores were obtained for the domains Scope and Purpose and Clarity and Presentation. The CPGs fared less well on Rigour of Development, Stakeholder Involvement, Editorial Independence and Applicability. The number of recommendations addressing the care of persistent symptoms following mild TBI was meager, with the exception of military guidelines. CONCLUSIONS: There is considerable variability in the quality of guidelines addressing mild TBI and, overall, the CPGs reviewed score lower on Rigour of Development than CPGs for other medical conditions. There is a clear need for clinical guidance on the management of individuals who experience persistent symptoms following mild TBI.
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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 | 0.102 | 0.358 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".