Spinal cord injury in Manitoba: a provincial epidemiological study
Bibliographic record
Abstract
OBJECTIVE: To define the epidemiological trends and identify populations at risk of traumatic and non-traumatic spinal cord injury (NTSCI) for the province of Manitoba, Canada. METHODS: We reviewed records retrospectively for subjects in three cohorts (1981-1985, 1998-2002, and 2003-2007). A total of 553 individuals with spinal cord injury (SCI) were studied for variables such as age, level of injury, severity of injury, First Nations (FN) status, and etiology of injury. RESULTS: Incidence of overall SCi has increased from 22.0 to 46.5 per million (P< 0.001). Incidence of NTSCI increased from 3.12 per million to 16.7 per million (P < 0.001). Incidence of traumatic spinal cord injury (TSCI) has increased from the 17.1 per million to 25.6 per million (P<0.001). There was a significant increase in the mean age at injury from 30.23 to 45.768 years of age (P < 0.0001). Female and NTSCI have a higher mean and median age at injury. There was a significant (P = 0.0008) increase in the proportion of females with a most recent male/female ratio of 3.4:1. A temporal increase in incomplete injuries was observed (P < 0.0001). Incomplete and thoracic level injuries are more common with NTSCI. CONCLUSION: The results demonstrate that there are significant differences between NTSCI and TSCI in Manitoba, and that Manitoba trends in SCI are in keeping with those seen on a national and an international level. There is a high risk of SCI in Manitoba FN, for which preventive strategies need to be put in place, and higher resource structure geared towards. Additionally, the trend of older age at injury has significant implications for structuring acute care and rehabilitation programs for these individuals, enhancing the need for treating older and more medically complicated individuals with SCI.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".