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Record W3092574995 · doi:10.1097/paf.0000000000000622

Xylazine, a Veterinary Tranquilizer, Detected in 42 Accidental Fentanyl Intoxication Deaths

2020· article· en· W3092574995 on OpenAlexaff
Jacqueline Nunez, Maura E. DeJoseph, James R. Gill

Bibliographic record

VenueAmerican Journal of Forensic Medicine & Pathology · 2020
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Pharmacology and Anesthesia
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsTranquilizerXylazineAccidentalMedicineAnesthesiaFentanylKetamine

Abstract

fetched live from OpenAlex

ABSTRACT: Xylazine is an emerging adulterant with fentanyl in fatal drug intoxications, which has public health, safety, and criminal investigative implications. Xylazine is a nonnarcotic sedative used for analgesia and muscle relaxation exclusively in veterinary medicine. Its chemical structure is similar to clonidine and acts as a central α-2 agonist which may cause bradycardia and transient hypertension followed by hypotension. We report the detection of xylazine in 42 deaths in Connecticut from March to August 2019. Xylazine combined with an opioid or stimulant may affect the toxicity of these drugs. Detection of xylazine may help the forensic pathologist distinguish illicit from prescribed fentanyl, and law enforcement agents track the illicit drugs to a specific drug supplier. Because of its lack of response to naloxone, emergency medicine physicians need to be aware of its potential presence as it may affect therapy.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.352
Teacher spread0.297 · 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 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

Citations77
Published2020
Admission routes1
Has abstractyes

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