Temporal trends in vascular trauma in Ontario, 1991-2009: a population-based study
Bibliographic record
Abstract
BACKGROUND: Vascular injuries add substantial complexity to trauma care, yet Canadian epidemiologic data on such injuries are not readily available. We conducted a retrospective cross-sectional study to examine temporal trends in the rate of hospital admissions for vascular injuries from 1991 to 2009 in Ontario. METHODS: Individuals of any age admitted to hospital because of vascular trauma in Ontario were included. Details of vascular injury and patient demographic characteristics were recorded, hospital admission rates estimated and analyses stratified by sex, age, mechanism of injury, economic status and geographic location. Time-series analysis was used to examine trends in hospital admission rates. RESULTS: Of the 8252 hospital admissions for vascular trauma, 4287 (52.0%) involved injuries to the upper limb and 1819 (22.0%) were due to transport-related causes. Overall, the annual rate declined significantly over time, from 3.3 per 100 000 in 1991 to 2.7 per 100 000 in 2009 (p < 0.01). The subgroups with the highest rates were young men, patients with a low economic status and those living in a rural location. Declines occurred in both sexes and in all age groups except those 65 years and older. The rates of vascular injury to the neck, thorax, upper limbs and lower limbs declined over time, but not the rate of abdominal vascular injury. Although the rate of non-transport-related vascular injuries declined, the rate of transport-related vascular injuries did not change significantly over time. Decreases in annual rates occurred in both low- and high-economic status groups and in urban populations but not in rural populations. INTERPRETATION: Overall, the annual rate of hospital admissions for vascular trauma declined significantly in Ontario over the study period. Our findings have important implications for public health and the development of injury-prevention strategies, particularly for population subgroups at high risk of vascular injury.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".