P.153 Traumatic spinal cord injuries among indigenous and non-indigenous peoples of Canada
Bibliographic record
Abstract
Background: Despite a higher prevalence of traumatic spinal cord injury (TSCI) amongst Canadian Indigenous peoples, there is a paucity of studies focused on Indigenous TSCI. We present the first Canada-wide study comparing TSCI amongst Canadian Indigenous and non-Indigenous peoples. Methods: This study is a retrospective analysis of prospectively-collected TSCI data from the Rick Hansen Spinal Cord Injury Registry (RHSCIR) from 2004-2019. We divided participants into Indigenous and non-Indigenous cohorts and compared them with respect to demographics, injury mechanism, level, severity, and outcomes. Results: Compared with non-Indigenous patients, Indigenous patients were younger, more female, less likely to have higher education, and less likely to be employed. The mechanism of injury was more likely due to assault or transportation-related trauma in the Indigenous group. The length of stay for Indigenous patients was longer. Indigenous patients were more likely to be discharged to a rural setting, less likely to be discharged home, and more likely to be unemployed following injury. Conclusions: Our results suggest that more resources need to be dedicated for transitioning Indigenous patients sustaining a TSCI to community living and for supporting these patients in their home communities. A focus on resources and infrastructure for Indigenous patients by engagement with Indigenous communities is needed.
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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 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".