The Epidemiology of Traumatic Spinal Cord Injury in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: To describe the incidence and pattern of traumatic spinal cord injury and cauda equina injury (SCI) in a geographically defined region of Canada. METHODS: The study period was April 1, 1997 to March 31, 2000. Data were gathered from three provincial sources: administrative data from the Alberta Ministry of Health and Wellness, records from the Alberta Trauma Registry, and death certificates from the Office of the Medical Examiner. RESULTS: From all three data sources, 450 cases of SCI were identified. Of these, 71 (15.8%) died prior to hospitalization. The annual incidence rate was 52.5/million population (95% CI: 47.7, 57.4). For those who survived to hospital admission, the incidence rate was 44.3/million/year (95% CI: 39.8, 48.7). The incidence rates for males were consistently higher than for females for all age groups. Motor vehicle collisions accounted for 56.4% of injuries, followed by falls (19.1%). The highest incidence of motor vehicle-related SCI occurred to those between 15 and 29 years (60/million/year). Fall-related injuries primarily occurred to those older than 60 years (45/million/year). Rural residents were 2.5 times as likely to be injured as urban residents. CONCLUSION: Prevention strategies for SCI should target males of all ages, adolescents and young adults of both sexes, rural residents, motor vehicle collisions, and fall prevention for those older than 60 years.
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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 | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".