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Record W7116752046 · doi:10.1097/paf.0000000000001105

National Association of Medical Examiners Position Paper

2025· article· en· W7116752046 on OpenAlexaff
James Gill, Elizabeth A. Bundock, Kristinza Giese, Cynthia K. Harris, Heather Jarrell, Michelle A. Jorden, Tara J. Mahar, Jennifer Love, Evan Matches, Deanna Oleske, Gregory A. Vincent

Bibliographic record

VenueAmerican Journal of Forensic Medicine & Pathology · 2025
Typearticle
Languageen
FieldMedicine
TopicChild Abuse and Related Trauma
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsDocumentationCertificationDiscretionMedical examinerPosition paperAssociation (psychology)Criminal justicePosition (finance)

Abstract

fetched live from OpenAlex

The National Association of Medical Examiners (NAME) convened a panel to create a position paper for the investigation of pediatric deaths due to suspected inflicted head trauma. The certification of both the cause and manner of death is dependent upon an evaluation of all available data including information derived from the investigation, scene, postmortem examination, and ancillary studies. This paper provides the forensic pathologist with a comprehensive review for the postmortem examination of infants and toddlers who have died or have apparently died of inflicted head trauma. Specifically, this paper describes (1) procedures, (2) ancillary laboratory tests, and (3) forms of documentation that may be important in the investigation of these deaths. Some of these techniques are highly specialized and are performed at the discretion of the prosector. The evaluation and documentation of such fatalities involves the production of a reviewable, objective data set to support the multitude of inquiries that may follow from the public and the criminal justice system.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.090
Threshold uncertainty score0.302

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0040.001
Scholarly communication0.0050.002
Open science0.0030.003
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0900.070

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 head0.007
GPT teacher head0.293
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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
Published2025
Admission routes1
Has abstractyes

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