Accidental cold-related injury leading to hospitalization in northern Sweden: an eight-year retrospective analysis
Bibliographic record
Abstract
BACKGROUND: Cold injuries are rare but important causes of hospitalization. We aimed to identify the magnitude of cold injury hospitalization, and assess causes, associated factors and treatment routines in a subarctic region. METHODS: In this retrospective analysis of hospital records from the 4 northernmost counties in Sweden, cases from 2000-2007 were identified from the hospital registry by diagnosis codes for accidental hypothermia, frostbite, and cold-water drowning. Results were analyzed for pre-hospital site events, clinical events in-hospital, and complications observed with mild (temperature 34.9 - 32°C), moderate (31.9 - 28°C) and severe (<28°C), hypothermia as well as for frostbite and cold-water drowning. RESULTS: From the 362 cases, average annual incidences for hypothermia, frostbite, and cold-water drowning were estimated to be 3.4/100,000, 1.5/100,000, and 0.8/100,000 inhabitants, respectively. Annual frequencies for hypothermia hospitalizations increased by approximately 3 cases/year during the study period. Twenty percent of the hypothermia cases were mild, 40% moderate, and 24% severe. For 12%, the lowest documented core temperature was 35°C or higher, for 4% there was no temperature documented. Body core temperature was seldom measured in pre-hospital locations. Of 362 cold injury admissions, 17 (5%) died in hospital related to their injuries. Associated co-factors and co-morbidities included ethanol consumption, dementia, and psychiatric diagnosis. CONCLUSIONS: The incidence of accidental hypothermia seems to be increasing in this studied sub-arctic region. Likely associated factors are recognized (ethanol intake, dementia, and psychiatric diagnosis).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".