In search of evidence-based treatment for concussion: characteristics of current clinical trials
Bibliographic record
Abstract
UNLABELLED: Abstract Objective: To assess the characteristics of current clinical trials investigating the treatment of concussion. BACKGROUND: Recent systematic literature reviews have concluded that there is minimal evidence to support any specific treatment for concussion, including the principles of return-to-activity protocols such as type or duration of rest. DESIGN/METHODS: Clinical trial data was extracted from Clinicaltrials.gov and seven additional World Health Organization primary registries. The trial databases were accessed up until 3 October 2013. This study used search terms of 'concussion' or 'mild traumatic brain injury' (mTBI) and filtered for interventional trials. Trials that were terminated, already published or not interventional trials of concussion/mTBI were excluded. RESULTS: Of the 142 concussion/mTBI interventional clinical trials identified, 71 met inclusion criteria. Trials had a median estimated enrolment of 60 participants. There was a wide-range of treatments studied, including cognitive/behavioural therapies (28.2%), medications (28.2%), devices (11.3%), dietary supplements (8.5%), return-to-activity/rest (1.4%) and others (22.4%). Heterogeneity among trials for concussion identification/diagnosis and primary outcomes utilized was evident. Symptom-based questionnaires (39.4%) and neuropsychological tests (28.2%) were the most common outcome measures. CONCLUSIONS: Diverse, potentially promising therapeutics are currently being studied for the treatment of concussion. However, several deficiencies were identified including a paucity of trials addressing return-to-activity principles. Also, small sample size and trial heterogeneity may threaten scientific evaluation and subsequent clinical application.
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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 | 0.151 | 0.505 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.029 | 0.052 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".