Ten-year outcomes following traumatic brain injury: A population-based cohort
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To quantify the 10 year health service use (HSU) and mortality outcomes for people with a traumatic brain injury (TBI). RESEARCH DESIGN: A population-based matched cohort study using linked administrative data from Manitoba, Canada (Manitoba Injury Outcome Study). METHODS AND PROCEDURES: An inception cohort (1988-1991) of hospitalized cases with TBI aged 18-64 years (n = 1290) was identified and matched to a non-injured comparison group (n = 1290). Survival analysis, Negative binomial and Poisson regression were used to quantify associations between injury and HSU/mortality outcomes for 10 years following the TBI event. MAIN OUTCOME AND RESULTS: The majority of deaths (47.2%) occurred in the first 60 days following injury. Excluding the first 60 days, the adjusted 10 year mortality remained elevated (mortality rate ratio = 1.48, 95% CI = 1.02-2.15). After adjusting for demographic characteristics and pre-existing health status, the TBI cohort had more post-injury hospitalizations (rate ratio (RR) = 1.54, 95% CI = 1.39-1.71), greater cumulative lengths of stay (RR = 5.14, 95% CI = 3.29-8.02) and a greater post-injury physician claims rate (RR = 1.44, 95% CI = 1.35-1.53) than the non-injured cohort. CONCLUSIONS: People who sustain a TBI and survive the initial acute phase of care experience substantially increased long-term morbidity compared to the general population, regardless of the level of injury severity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".