Impact of Specialized Versus Non-Specialized Acute Hospital Care on Survival Among Patients With Acute Incomplete Traumatic Spinal Cord Injuries: A Population-Based Observational Study from British Columbia, Canada
Bibliographic record
Abstract
Given the complexity of care necessitated after an acute traumatic spinal cord injury (SCI), it seems intuitively beneficial for such care to be delivered at hospitals with specialized SCI expertise. Demonstrating these benefits is not straightforward, however. We sought to determine whether specialized acute hospital care influenced the most fundamental outcomes after SCI: mortality within the first year of injury. We compared survival among patients with incomplete tSCI admitted to a single quaternary-level trauma hospital with a specialized acute SCI program versus those admitted to trauma hospitals without specialized acute SCI care. We performed a population-based retrospective observational cohort study using administrative and clinical data linked from multiple sources in British Columbia (BC) from 2001 to 2017. Among a cohort of 1920 patients, there were 193 deaths within one year. We failed to identify a significant overall benefit for survival after adjusting for potential confounders, and the confidence intervals (CIs) were compatible with both benefit and harm (odds ratio [OR] 1.01, 95% CI 0.17 to 6.11, p = 0.99). Significant associations were observed with age greater than 65 (OR 4.92, 95% CI 1.66 to 14.57, p < 0.01), Charlson Comorbidity Index (OR 1.61, 95% CI 1.42 to 1.83, p < 0.01), Injury Severity Score (OR 1.08, 95% CI 1.06 to 1.11, p < 0.01), and traumatic brain injury (OR 2.12, 95% CI 1.32 to 3.41, p < 0.01). Among patients with acute tSCI, admission to a hospital with specialized acute SCI care was not associated with improved overall one-year survival. Subgroup analyses, however, suggested heterogeneity of effects, with little benefit for older patients with less polytrauma and substantial benefit for younger patients with greater polytrauma.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".