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Record W2077546614 · doi:10.1111/1556-4029.12165

Characteristics of Medical Examiner/Coroner Offices Accredited by the National Association of Medical Examiners

2013· article· en· W2077546614 on OpenAlexaff
Mitchell Weinberg, Victor W. Weedn, Seth H. Weinberg, David R. Fowler

Bibliographic record

VenueJournal of Forensic Sciences · 2013
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsOffice of the Chief Medical Examiner
FundersBureau of Justice Statistics
KeywordsMedical examinerCoronerStaffingMedicinePopulationAccreditationHomicideForensic scienceDemographyWorkloadMedical emergencyFamily medicinePoison controlInjury preventionEnvironmental healthVeterinary medicineManagementMedical educationNursing

Abstract

fetched live from OpenAlex

The National Association of Medical Examiners accredits medical examiner and coroner offices. Approximately 60 offices were fully or provisionally accredited as of late 2011, and these offices serve one-quarter of the U.S. population. The calculated average population served was 1.6M but ranged from 0.3 to 10.5M. The calculated mean death rate was 794 deaths/100K population, and the mean homicide rate was 7.2 homicides/100K population. The calculated mean budget was $4.35M, but budgets ranged from $0.67 to $26.8M. The calculated mean budget/capita was $3.02 but ranged from $0.62 to $10.22. The average size of the facility was under 30,000 sq. ft. The calculated average staffing was found to include five forensic pathologists, four and a half autopsy technicians, and nine investigators. The mean forensic pathologists/1M population was 3.7. Calculated workload indices included 222 autopsies/pathologist and an autopsy rate of 77.6/100K population. These results show that offices of every size can achieve accreditation.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.786
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.254
Teacher spread0.238 · 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

Citations15
Published2013
Admission routes1
Has abstractyes

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