Vascular injury-related in-hospital mortality in Ontario between 1991 and 2009
Bibliographic record
Abstract
OBJECTIVE: Trauma-related vascular injuries are major contributors to morbidity and mortality worldwide. We conducted a retrospective, population-based, cross-sectional study to examine temporal trends and factors associated with traumatic vascular injury-related in-hospital mortality in Ontario, Canada from 1991 to 2009. METHODS: We obtained data on Ontario hospital admissions for traumatic vascular injury, including injury mechanism and body region; and patient age, sex, socioeconomic status, and residence from the Canadian Institute for Health Information Discharge Abstract Database and Registered Persons Database from fiscal years 1991 to 2009. We performed time series analysis of vascular injury-related in-hospital mortality rates and multivariable logistic regression analysis to identify significant mortality-associated factors. RESULTS: The overall in-hospital mortality rate for trauma-related vascular injury was 5.5%. A slight but non-significant decline in mortality occurred over time. The likelihood of vascular injury-related in-hospital mortality was significantly higher for patients involved in transport-related accidents (odds ratio [OR[=2.21, 95% confidence interval [CI], 1.76-2.76), age ≥65 years (OR = 4.34, 95% CI, 2.25-8.38), or with thoracic (OR = 2.24, 95% CI, 1.56-3.20) or abdominal (OR = 2.45, 95% CI, 1.75-3.42) injuries. CONCLUSIONS: In-hospital mortality from traumatic vascular injury in Ontario was low and stable from 1991 to 2009.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".