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Record W4318475980 · doi:10.1111/1556-4029.15213

The use of serum protein analysis in the diagnosis of fatal envenomation via <i>Crotalus horridus</i> (timber rattlesnake)

2023· article· en· W4318475980 on OpenAlexaff
Tim Gallagher, Stephen M. Roberts, Cecilia Silva-Sánchez, Lerah Sutton, Kaitlyn Laventure

Bibliographic record

VenueJournal of Forensic Sciences · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVenomous Animal Envenomation and Studies
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsCrotalusEnvenomationVenomMedicineBiologyEcology

Abstract

fetched live from OpenAlex

Deaths occurring due to rattlesnake envenomization are extremely rare and must be thoroughly investigated in the same manner as any other type of death. Our research presents the case of an adult white male who suffered a fatal timber rattlesnake (Crotalus horridus) envenomation in northwest Florida in 2018. Blood samples were taken from the decedent's heart and vasculature of the chest and sent for serum proteomic analysis. Serum proteomic analysis was utilized in order to identify proteins from timber rattlesnake (C. horridus) found within the victim's blood. The confirmation of the presence of timber rattlesnake venom within the victim's blood allows the forensic pathologist to determine the cause of death most accurately and likewise, assists with the manner of death determination. Blood samples were separated into two groups: one with the abundant endogenous proteins depleted to facilitate detection of lower abundant proteins and one undepleted. In the depleted sample, a total of 712 proteins were identified, with 47 of the proteins (6.6%) occurring originating from timber rattlesnake (C. horridus). Likewise, a total of 742 proteins were identified in the undepleted sample, with 52 of the proteins (7.0%) occurring in timber rattlesnake (C. horridus). No timber rattlesnake (C. horridus) proteins were found in control human serum.

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)0.001
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 score0.221
Threshold uncertainty score0.139

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.043
GPT teacher head0.281
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

Citations2
Published2023
Admission routes1
Has abstractyes

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