Predictors of Postacute Mortality Following Traumatic Brain Injury in a Seriously Injured Population
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) is a primary cause of injury mortality in developed countries but less is known about the impact of TBI on postacute mortality in large study populations. This study investigates the rate and predictors of postacute mortality (1-9 years after the initial injury) of severely injured persons with TBI in the Province of Ontario from April 1, 1993 to March 31, 1995. METHOD: Cases were identified (n = 2,721) from the Ontario Trauma Registry Comprehensive Data Set based on lead trauma hospitals in the province which also provided data on predictors. Severely injured patients (n = 557) who had lower extremity injuries during the sample time period formed a control population. RESULTS: Poisson regression modeling showed that having a TBI was a significant predictor of premature death controlling for age and injury severity. Age, the number of comorbidities, injury severity, mechanism of injury, and discharge destination were significant predictors in the multivariate analyses for the TBI population. CONCLUSIONS: This research quantifies the elevated risk of premature death in the postacute period for seriously injured adults with TBI and identifies factors most associated with highest mortality rates in this population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".