Trends in Hospitalization Associated With Traumatic Brain Injury in a Publicly Insured Population, 1992–2002
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) is a leading cause of death and disability in developed countries. We document trends in TBI-related hospitalizations in Ontario, Canada, between April 1992 and March 2002, focusing on relationships between inpatient hospitalization rates, age, sex, cause of injury, severity level, and in-hospital mortality. METHODS: Information on all acute hospital separations in Ontario with a diagnosis of TBI was analyzed using logistic regression. RESULTS: Hospitalization rates fell steeply among children and young adults but remained stable among adults aged 66 and older. The proportion of TBI hospitalizations with mild injuries decreased from 75% to 54%, whereas the proportion with moderate injuries increased from 19% to 37%. Adjusting for other risk factors, in-hospital deaths were higher for injuries because of motor vehicle crashes than those because of falls. In-hospital death rates were stable for patients with moderate or severe injuries, but increased over time among those whose injuries were classified as mild, suggesting a trend toward more serious injury within the "mild" classification. CONCLUSIONS: Hospitalizations for TBI involve fewer mild injuries over time and are highest in the oldest segment of the 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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".