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Record W2278628673 · doi:10.1097/paf.0000000000000192

Companion Cases in a Large Urban Medical Examiner's Office

2015· article· en· W2278628673 on OpenAlexaff
Leigh Hlavaty, Chantel Njiwaji, LokMan Sung

Bibliographic record

VenueAmerican Journal of Forensic Medicine & Pathology · 2015
Typearticle
Languageen
FieldMedicine
TopicRestraint-Related Deaths
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsMedical examinerNatural deathAccidentalHomicideManner of deathMedicineCompanion animalCause of deathMedical emergencyDemographyInjury preventionPoison controlPathologyDisease

Abstract

fetched live from OpenAlex

Companion death cases, as defined in this study, include 2 or more deaths that occur at the same location or 1 death at a specific location combined with 1 or more individuals transported from that same location to a hospital where death was pronounced within 1 hour of arrival. These types of cases can have multiple causes and manners of death. The Wayne County Medical Examiner's Office conducted a retrospective study of companion death cases that came into the office from mid 2007 to the end of 2014. The purpose of the study was to identify and examine patterns of companion death cases in a large urban area that would assist future companion death case investigations. Three hundred fifty deaths were found to be companion cases, including 135 pairs (2 connected deaths in the same location), 20 trios, and 5 quartets. Approximately 49% of companion case deaths were homicides. Approximately 30% of companion case deaths were traumatic accidental deaths. Around 14% of companion case deaths that were from the same scene location had different manners of death, including suicide, homicide, natural, and indeterminate. The remainder of companion death cases were either drug related or natural. Through this study, we have identified a pattern to these companion death cases and have concluded that it is important to conduct a thorough medicolegal death investigation of such cases to establish and elucidate the true circumstances surrounding these deaths.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.146
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.320
Teacher spread0.289 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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