Traumatic Spinal Cord Injury in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Few population-based analyses of spinal cord injuries exist from which to base Canadian prevention initiatives. This study aimed to calculate rates of traumatic spinal cord injury for the province of Ontario and describe these injuries by several epidemiologic parameters. METHODS: Two thousand three hundred eighty-five hospital admissions were studied for April 1, 1994, through March 31, 1999. RESULTS: Annual age-standardized rates declined from a maximum of 46.2 hospitalizations per 1 million population (95% confidence interval, 42.1-50.3) to 37.2 per 1 million (95% confidence interval, 33.8-41.0). Male rates declined over the study period, whereas female rates remained stable. Leading external causes included unintentional falls (1,030 of 2,385 [43.2%]), especially among the elderly, and transport injuries (1,021 of 2,385 [42.8%]), especially among those aged less than 40 years. Intentional injuries were most commonly seen among those aged 20 to 39 years (48 of 86 [55.8%]). Misclassification of some elder fall cases as spinal cord injuries is a methodologic concern. CONCLUSION: The results indicate the relative importance of several external causes of injury and are useful in establishing rational priorities for prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.004 | 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".