Emergency department prediction of post-concussive syndrome following mild traumatic brain injury—an international cross-validation study
Bibliographic record
Abstract
BACKGROUND: Between 20-50% of those suffering a mild traumatic brain injury (MTBI) will suffer symptoms beyond 3 months or post-concussive disorder (PCD). Researchers in Sydney conducted a prospective controlled study which identified that bedside recordings of memory impairment together with recordings of moderate or severe pain could predict those who would suffer PCS with 80% sensitivity and specificity of 76%. PRIMARY OBJECTIVE: This study is a cross-validation study of the Sydney predictive model conducted at Montreal General Hospital, Montreal, Canada. METHODS: One hundred and seven patients were assessed in the Emergency Department following a MTBI and followed up by phone at 3 months. The Rivermead Post-Concussive Questionnaire was the main outcome measure. RESULTS: Regression analysis showed that immediate verbal recall and quantitative recording of headache was able to predict PCD with a sensitivity of 71.4% and a specificity of 63.3%. In the combined MTBI groups from Sydney and Montreal the sensitivity was 70.2% and the specificity was 64.2%. CONCLUSION: This is the first study to compare populations from different countries with diverse language groups using a predictive model for identifying PCD following MTBI. The model may be able to identify an 'at risk' population to whom pre-emptive treatment can be offered.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".