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Record W2071305656 · doi:10.3402/ijch.v71i0.18502

Fatal hypothermia: an analysis from a sub-arctic region

2012· article· en· W2071305656 on OpenAlexaff
Helge Brändström, Anders Eriksson, Gordon G. Giesbrecht, Karl‐Axel Ängquist, Michael Haney

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2012
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineIncidence (geometry)HypothermiaAccidental hypothermiaEmergency medicineDemographyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine the incidence as well as contributing factors to fatal hypothermia. STUDY DESIGN: Retrospective, registry-based analysis. METHODS: Cases of fatal hypothermia were identified in the database of the National Board of Forensic Medicine for the 4 northernmost counties of Sweden and for the study period 1992-2008. Police reports, medical records and autopsy protocols were studied. RESULTS: A total of 207 cases of fatal hypothermia were noted during the study period, giving an annual incidence of 1.35 per 100,000 inhabitants. Seventy-two percent occurred in rural areas, and 93% outdoors. Many (40%) were found within approximately 100 meters of a building. The majority (75%) occurred during the colder season (October to March). Some degree of paradoxical undressing was documented in 30%. Ethanol was detected in femoral vein blood in 43% of the victims. Contributing co-morbidity was common and included heart disease, earlier stroke, dementia, psychiatric disease, alcoholism, and recent trauma. CONCLUSIONS: With the identification of groups at high risk for fatal hypothermia, it should be possible to reduce risk through thoughtful interventions, particularly related to the highest risk subjects (rural, living alone, alcohol-imbibing, and psychiatric diagnosis-carrying) citizens.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.034
GPT teacher head0.353
Teacher spread0.319 · 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 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".

Quick stats

Citations37
Published2012
Admission routes1
Has abstractyes

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