Impact of Housing on Burn Injury Patterns and Outcomes: A Retrospective Cohort Study at a Canadian Burn Center
Bibliographic record
Abstract
Houselessness is an important social determinant of health, which may predispose to poor health and difficulty in recovering from new health issues. We examined the demographic variables and clinical outcomes of housed and unhoused patients experiencing thermal injuries. A retrospective chart review was performed on all patients admitted to or followed by the burn service from January 2022 to June 2024. There were 571 new thermal injuries requiring admission at our institution, including 414 patients with housing and 157 unhoused patients. Frostbite accounted for 35% of admissions among unhoused patients, which was significantly greater than the 10% of housed patient admissions for frostbite injuries (P < .0001), where thermal burns accounted for the majority of injuries requiring admission. Substance use was significantly higher in the unhoused population (P < .0001). Unhoused patients on the burn service were 10-fold more likely to leave against medical advice, compared with housed patients (P < .00001). Interestingly, the mean length of stay was not significantly different between the housed and unhoused inpatients; however, it was significantly different with the exclusion of patients who left against medical advice (18.21 ± 30.84 days vs 24.70 ± 26.30 days; P = .03). The houseless population in Canada experiences unique challenges due to the extreme cold, while their inpatient-care providers are challenged by a resource-constrained system. These results suggest the need for additional support for thermal injury prevention and substance use disorder treatment among the unhoused population.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".