Incidence of Spinal Cord Injury Worldwide: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Incidence studies of spinal cord injury (SCI) are important for health-care planning and epidemiological research. This review gives a quantitative update on SCI epidemiology worldwide through a statistical evaluation of incidence rates. METHODS: A systematic review was conducted. For each study, the crude rate ratio was calculated and, when possible, age- and gender-adjusted incidence rate ratios with 95% CI were determined by direct adjustment or using Poisson regression. RESULTS: Thirteen studies were included. Annual crude incidence rates in traumatic SCI varied from 12.1 per million in The Netherlands to 57.8 per million in Portugal. Compared to the Portuguese reference study, incidence rates showed a 3-fold variation, with the highest rates in Canada and Portugal. Most traumatic SCI studies showed a bimodal age distribution. The first peak was found in young adults between 15 and 29 years and a second peak in older adults (mostly > or = 65 years). Motor vehicle accidents and falls were the most prevalent causes of injury accounting for nearly equal percentages. In contrast, another age pattern in non-traumatic SCI reflected steadily increasing incidence with advancing age. CONCLUSIONS: The results show significant variation in SCI incidence with changing epidemiological patterns. A trend towards increased incidence in the elderly was observed, likely due to falls and non-traumatic 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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".