Three-Month Outcomes of Traumatic Brain Injury in the General Population: A Sunnybrook Traumatic Brain Injury Cohort Study
Bibliographic record
Abstract
Traumatic brain injury (TBI) is common, disabling, and a growing public health concern. There are limited large-scale studies providing insight into factors associated with recovery in the general TBI population. Our aim was to identify factors associated with concussion/TBI symptom severity and return-to-work. We performed a prospective cohort study of concussion/TBI (predominantly mild to moderate) patients with data collected over a 20-year period (1998–2018). This is the first study presenting data from the Sunnybrook TBI (SUNTBI) cohort. Primary outcome at approximately 3-month postinjury was the Rivermead Post-Concussion Symptoms Questionnaire (RPQ), and secondary outcome was return-to-work. Outcomes were analyzed using multivariable linear regression and logistic regression models, respectively. There were 2924 TBI patients included in the study. General Health Questionnaire (GHQ), a screening measure of current psychiatric symptoms, and all its subscales (depression, anxiety, somatic, and social) ( p < 0.0001), and active litigation ( p < 0.001) were significantly associated with higher RPQ scores. Notably, factors related to injury characteristics and severity were not (e.g., injury mechanism, TBI severity, and neuroimaging abnormalities). For return-to-work, having a professional occupation ( p < 0.001) was significantly positively associated with return, while abnormal CT scan ( p = 0.001), admission to hospital ( p < 0.001), and higher GHQ score ( p < 0.001) were negatively associated. In one of the largest observational studies of general population concussion/TBI patients to date, we found that psychiatric symptoms and litigation status were significantly associated with symptoms at 3 months, while factors related to the injury severity were not. We also observed a decoupling of factors that impact symptom score outcomes from return-to-work outcomes. These results have important implications for the management of at-risk TBI subpopulations and wider public policy considerations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".