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Record W4200202582 · doi:10.1097/paf.0000000000000741

Forensic DNA Typing From Femurs and Bones of the Foot: A Study of 3 Cases.

2022· article· en· W4200202582 on OpenAlexaff
Heitor Simões Dutra Corrêa, Venusia Cortellini, Lorenzo Franceschetti, Andrea Verzeletti

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsPhalanxFoot (prosody)FemurTypingAnatomyNail (fastener)Cadaveric spasmBiologyOrthodonticsMedicineSurgeryGeneticsEngineeringStructural engineering

Abstract

fetched live from OpenAlex

ABSTRACT: Evidence has been accumulating in the sense that femur may not always be the best option for DNA typing of skeletal remains. Recent studies have shown that bones of the hands and feet appear to be a superior source of preserved DNA. The current study reanalyzed DNA quantitation, degradation, and short tandem repeat typing in femurs, lateral cuneiforms, and distal foot phalanges. Data from 3 human identification cases involving corpses in an advanced decomposition state were collected. We found that in the studied cases, the femur provided equal or inferior results, recovering 84.9% of true alleles. Lateral cuneiforms (99.2%) and distal foot phalanges (96.8%) yielded higher percentages. In addition, more drop-ins and drop-outs were detected in femurs than cuneiforms and phalanges. This study adds to current findings that advocate for further investigation into bone selection for use in forensic practice. The impacts of our findings are limited by the small number of individuals studied and may not apply to old and degraded bones.

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)0.000
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 score0.367
Threshold uncertainty score0.164

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.025
GPT teacher head0.242
Teacher spread0.217 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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