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Record W2580932839 · doi:10.1111/1556-4029.13412

Evaluation of Acute Alcohol Intoxication as the Primary Cause of Death: A Diagnostic Challenge for Forensic Pathologists

2017· article· en· W2580932839 on OpenAlexaff
Rong Li, Hu Li, Lingli Hu, Xiang Zhang, Rebecca Phipps, David R. Fowler, Feng Chen, Ling Li

Bibliographic record

VenueJournal of Forensic Sciences · 2017
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsOffice of the Chief Medical Examiner
FundersChina University of Political Science and LawNational Natural Science Foundation of China
KeywordsMedicineAutopsyForensic pathologyBinge drinkingCause of deathOverweightAlcohol intoxicationPopulationUrinePoison controlInjury preventionInternal medicineEmergency medicineObesityEnvironmental healthDisease

Abstract

fetched live from OpenAlex

Deaths caused by acute alcohol intoxication (AAI) remain a major public health issue. This study is retrospective and descriptive: an 8-year case analysis of deaths due to AAI in Maryland. Study showed that of 150 AAI deaths, the death rate among Hispanics (10.41/100,000 population) was significantly higher than all the non-Hispanics combined (1.88/100,000 population). The majority of individuals were young adults, overweight, and binge drinkers. The obese group showed significantly lower mean heart and peripheral blood alcohol concentration (BAC) (0.36%, 0.37%) than the normal weight group (0.45%, 0.42%). Based on the PBAC and urine AC ratio, 49.6% deaths likely occurred close to peak phase, followed by postabsorptive phase (31.6%) and absorptive phase (18.8%). Our results indicate that forensic pathologists should evaluate postmortem BAC in the light of individual's age, drinking history, body weight, possible phase of alcohol intoxication, and other autopsy findings when certifying AAI as primary cause of death.

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)0.007
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.297
GPT teacher head0.479
Teacher spread0.183 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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