Does head<scp>CT</scp>scan pathology predict outcome after mild traumatic brain injury?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: More evidence is needed to forward our understanding of the key determinants of poor outcome after mild traumatic brain injury (MTBI). A large, prospective, national cohort of patients was studied to analyse the effect of head CT scan pathology on the outcome. METHODS: One-thousand two-hundred and sixty-two patients with MTBI (Glasgow Coma Scale score 15) at 39 emergency departments completed a study protocol including acute head CT scan examination and follow-up by the Rivermead Post Concussion Symptoms Questionnaire and the Glasgow Outcome Scale Extended (GOSE) at 3 months after MTBI. Binary logistic regression was used for the assessment of prediction ability. RESULTS: In 751 men (60%) and 511 women (40%), with a mean age of 30 years (median 21, range 6-94), we observed relevant or suspect relevant pathologic findings on acute CT scan in 52 patients (4%). Patients aged below 30 years reported better outcome both with respect to symptoms and GOSE as compared to patients in older age groups. Men reported better outcome than women as regards symptoms (OR 0.64, CI 0.49-0.85 for ≥3 symptoms) and global function (OR 0.60, CI 0.39-0.92 for GOSE 1-6). CONCLUSIONS: Pathology on acute CT scan examination had no effect on self-reported symptoms or global function at 3 months after MTBI. Female gender and older age predicted a less favourable outcome. The findings support the view that other factors than brain injury deserve attention to minimize long-term complaints after MTBI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".