Therapy and rehabilitation of mild brain injury/concussion: Systematic review
Bibliographic record
Abstract
BACKGROUND: Assessment of therapies for the key consequences of mild traumatic brain injury (mTBI)/concussion is required. OBJECTIVE: Identify all RCTs of mTBI/concussion therapy, risks of bias, and therapies with significant positive results. METHODS: 17 electronic, 9 grey-literature databases searched without language/date restrictions; independent assessment of 1450 Abstracts/titles, 141 fulltext articles, 14 included RCTs. RESULTS: Four RCTs used American Congress of Rehabilitation TBI definition, others used unique definitions. Risk of bias: 43% low risk randomization; 14% concealed assignments; 21% blinded participants/personnel; 57% blinded assessors; 64% low risk attrition; 100% no selective reporting. Eleven RCTs included only mTBI. Ten significant positive results: six cognitive behavioral therapy (CBT), three videotape, pagers or personal digital assistants, and one physical therapy. One of referrals to health professionals no significant positive results. Three RCTs included both mTBI and moderate TBI. We wished to assess if authors proved using same interventions with both groups is appropriate. Two used CBT, one used pagers. All three RCTs significant positive results but results for their mild and moderate TBI patients were not separated. Two RCTs assessed return to work and no differences between intervention. CONCLUSION: Of 14 RCTs, six CBT, four digital assistants or videotape feedback and one physical therapy all had significant positive results. One referred patients to consultants and no significant positive results. Two assessed return to employment and no differences between interventions. Limitations are: (1) only four RCTs used the same concussion definition; (2) average age 38 (except for one study of adolescents, (3) all studies used unique interventions; (4) most authors used multiple interventions and effects could not be separated; (5) substantial attrition from eligibles to randomization, (4) only 64% at low risk from randomization, (5) 80 different outcome measures and meta-analysis was not possible, (6) only two studies assessed return to work.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".