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Record W4409023507 · doi:10.1093/jbcr/iraf019.284

655 Assessing Housing Status of Inpatient Admissions for Thermal Injuries: Insights from a Canadian Burn Unit

2025· article· en· W4409023507 on OpenAlexaffabout
Justin Lee, Izza Sattar, Shawn Dodd, Trent Schimmel, Sharada Manchikanti, Joshua N. Wong, Alexis Armour

Bibliographic record

VenueJournal of Burn Care & Research · 2025
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsAlberta Health ServicesUniversity of Alberta
Fundersnot available
KeywordsMedicineBurn unitsUnit (ring theory)Emergency medicineThermal burnMedical emergencySurgery

Abstract

fetched live from OpenAlex

Abstract Introduction Houselessness has increased in urban regions since the Covid-19 pandemic, potentially posing a risk to the population in the northern climate. This study investigates how pre-injury living conditions influence health outcomes among thermally injured inpatients post-pandemic. We hypothesized that housing status at the time of injury and admission is a significant determinant of health in our patient population. Methods A retrospective chart review was completed on patients admitted to the burn service at a level one trauma centre from January to December 2023. We examined demographics, injury types, and disposition among housed and unhoused inpatients. Primary outcome assessed was the total length of hospital stay (LOS). Secondary outcomes included frequency of discharges against medical advice (AMA). Statistical analysis was performed using t-test for continuous and chi-squared test for categorical variables. Results Of 214 new admissions in 2023, there was a greater proportion of burn injuries at 196 (91.6%) compared to frost injuries at 18 (8.4%) (p = 0.001). The rate of thermal and frost burn requiring hospitalization for unhoused individuals was estimated at 2025 and 437 per 100,000 persons, respectively. Significantly greater proportion of housed vs unhoused patients experienced thermal burn injuries (145 vs 51, respectively). In contrast, the unhoused population experienced a greater number of frost injuries, versus the housed population (11 vs 7, p = 0.001). The unhoused population had a significantly greater frequency of leaving against medical advice (35.4%), compared to the housed (4.9%, P < 0.0001). When excluding the patients who left AMA from the analysis, the mean length of stay in the housed and unhoused burn service inpatients (17.2, and 21.7 days, respectively) was not significantly different (p = 0.15). Conclusions In 2023, 26% of new thermal injury admissions were experiencing houselessness, demonstrating an increase compared to reported pre-pandemic levels. Our preliminary data demonstrate higher rates of unstable housing in frost injuries, compared with burn injuries, with a significant number of both groups leaving the hospital AMA. We plan to perform secondary outcome variable analyses to assess for contributing factors, while expanding our sample to include 2022 and 2024. In conclusion, housing status has a disproportionate effect on frostbite admissions in the northern climate, showing the significance of injury prevention with housing initiatives in Canada. Applicability of Research to Practice Future analyses will include quality improvement projects to identify factors contributing to higher rates of AMA in unhoused patients, as well as cost-benefit analyses of healthcare costs vs temporary housing to inform Canadian policy decisions regarding this vulnerable population. Funding for the Study N/A

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.077
GPT teacher head0.428
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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