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Record W4407698888 · doi:10.1080/10530789.2025.2463149

Frostbite and hypothermia among individuals experiencing homelessness in the south interior region of BC: a chart review of emergency department presentations

2025· review· en· W4407698888 on OpenAlexaff
Leanne Perrich, Silvina C. Mema, Stephanie Laing, John R. Graham, Matthew Gaudet

Bibliographic record

VenueJournal of Social Distress and the Homeless · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaInterior Health
Fundersnot available
KeywordsFrostbiteEmergency departmentHypothermiaMedicineChartEmergency medicineMedical emergencyPsychiatryAnesthesiaSurgeryStatistics

Abstract

fetched live from OpenAlex

Experiencing homelessness is a risk factor for prolonged exposure to cold weather potentially resulting in cold-related conditions (frostbite and hypothermia). The British Columbia (BC) Interior Health (IH) region experienced unseasonably cold weather in the month of December 2022 leading to patchwork community efforts to mitigate harms to the increasing number of vulnerable individuals. The goal of this study is to quantify and review circumstances around emergency department (ED) presentations for cold-related conditions in the IH region during the unseasonably cold month of December 2022 with a specific focus on those experiencing homelessness. We conducted a chart review of patients with cold-related conditions that presented to IH EDs, 1–31 December 2022. There were 77 ED presentations for cold-related conditions to 21 IH EDs, 12 rural and 9 urban, in the study period, with 62 visits presenting as frostbite/cold injury and 15 as hypothermia. Individuals experiencing homelessness accounted for 39% (30/77) of the total presentations, but up to 56.3% (27/48) of urban ED presentations and 10.3% (3/29) of rural ED presentations. Our chart review confirms that individuals experiencing homelessness are at high risk of cold-related conditions particularly in urban communities in the IH region.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.704
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

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

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 head0.057
GPT teacher head0.358
Teacher spread0.301 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

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