Incidence, risk factors and prevention of mild traumatic brain injury: results of the who collaborating centre task force on mild traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: We undertook a best-evidence synthesis on the incidence, risk factors and prevention of mild traumatic brain injury. METHODS: Medline, Cinahl, PsycINFO and Embase were searched for relevant articles. After screening 38,806 abstracts, we critically reviewed 169 studies on incidence, risk and prevention, and accepted 121 (72%). RESULTS: The accepted articles show that 70-90% of all treated brain injuries are mild, and the incidence of hospital-treated patients with mild traumatic brain injury is about 100-300/100,000 population. However, much mild traumatic brain injury is not treated at hospitals, and the true population-based rate is probably above 600/100,000. Mild traumatic brain injury is more common in males and in teenagers and young adults. Falls and motor-vehicle collisions are common causes. CONCLUSION: Strong evidence supports helmet use to prevent mild traumatic brain injury in motorcyclists and bicyclists. The mild traumatic brain injury literature is of varying quality, and the studies are very heterogeneous. Nevertheless, there is evidence that mild traumatic brain injury is an important public health problem, but we need more high-quality research into this area.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".