Incidence and Prevalence of Spinal Cord Injury in Canada: A National Perspective
Bibliographic record
Abstract
BACKGROUND: Despite decades of research, there are no national estimates of the incidence or prevalence of spinal cord injury (SCI) in Canada. Our objective was to utilize the best available data to estimate the incidence and prevalence of traumatic SCI (TSCI) and non-traumatic SCI (NTSCI) in Canada for 2010. METHODS: Initial incidence (number of TSCIs at injury scene) and discharge incidence (number discharged into the community) were calculated using published TSCI rates from Alberta and NTSCI rates from Australia. Prevalence was estimated by applying TSCI and NTSCI discharge incidence rates to historical Canadian population demographics using a cohort survival model and age-specific mortality rates for tetraplegia and paraplegia. RESULTS: The estimated 2010 initial incidence of TSCI is 1,785 cases per year, and the discharge incidence is 1,389 (41 per million). The estimated discharge incidence for NTSCI is 2,286 cases (68 per million). The prevalence of SCI in Canada is estimated to be 85,556 persons (51% TSCI and 49% NTSCI). CONCLUSIONS: This study provides the first estimates of the incidence and prevalence of SCI in Canada. More population-based studies are needed, particularly for NTSCI, as an increasing number of Canadians are expected to be affected by 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".