Outcomes of major trauma among patients with chronic kidney disease and receiving dialysis in Nova Scotia: a retrospective analysis
Bibliographic record
Abstract
BACKGROUND: The risk of death and complications after major trauma in patients with chronic kidney disease (CKD) is higher than in the general population, but whether this association holds true among Canadian trauma patients is unknown. OBJECTIVES: To characterize patients with CKD/receiving dialysis within a regional major trauma cohort and compare their outcomes with patients without CKD. METHODS: ) or receiving dialysis were identified by cross-referencing two regional databases for nephrology clinics and dialysis treatments. The primary outcome was in-hospital mortality; secondary outcomes included hospital/intensive care unit (ICU) length of stay (LOS) and ventilator-days. Cox regression was used to adjust for the effects of patient characteristics on in-hospital mortality. RESULTS: In total, 6237 trauma patients were identified, of whom 4997 lived within the regional nephrology catchment area. CKD/dialysis trauma patients (n=101; 28 on dialysis) were older than patients without CKD (n=4896), with higher rates of hypertension, diabetes, and cardiovascular disease, and had increased risk of in-hospital mortality (31% vs 11%, p<0.001). No differences were observed in injury severity, ICU LOS, or ventilator-days. After adjustment for age, sex, and injury severity, the HR for in-hospital mortality was 1.90 (95% CI 1.33 to 2.70) for CKD/dialysis compared with patients without CKD. CONCLUSION: Independent of injury severity, patients without CKD/dialysis have significantly increased risk of in-hospital mortality after major trauma.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".