Risk factors for complications in traumatic spinal cord injury: A retrospective analysis of a cohort of patients identified from administrative data
Bibliographic record
Abstract
STUDY DESIGN: Retrospective data analysis of a population-based observational cohort. Setting: TSCI in British Columbia, Canada Participants: 3,433 TSCI patients included in the study. METHODS: Hospital records linked with administrative databases were utilized to measure in-hospital mortality, adverse event rate, and LOS between 2001 and 2021. Adverse events included all documented complications during hospital admission. Multivariable logistic regression and Cox proportional hazard models were used to identify factors associated with mortality, adverse events, and LOS. RESULTS: All cause in-hospital mortality was 6.4%. The average age of patients was 53.2 years (SD 19.7), 75.4% were males and 70% incurred a cervical spinal cord injury. Multivariable analysis demonstrated that patients 35 years old, multiple medical comorbidities, cervical injury, neurologically complete, high injury severity score (25), concomitant brain injury, lower socioeconomic status, and no surgical management were at higher risk for death. Factors associated with adverse events were similar with the exception of non-operative patients who had lower adverse events. Cox modeling for LOS demonstrated similar findings to mortality analysis. CONCLUSIONS: This study found that spinal cord injury patients were more likely to have adverse outcomes with older age, cervical injury, multiple comorbidities, complete neurological injury, or higher severity initial traumatic injuries. This study identified risks associated with complications in TSCI, future research should address ways to improve outcomes in these targeted groups.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".