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Record W4401208140 · doi:10.1093/jat/bkae062

Fluorofentanyl and novel synthetic opioids in accidental overdose deaths

2024· article· en· W4401208140 on OpenAlexaff
Jihau Yu, Katie Diekhans, Anson Tsang, Luke N. Rodda

Bibliographic record

VenueJournal of Analytical Toxicology · 2024
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsOffice of the Chief Medical Examiner
FundersCalifornia Department of Public Health
KeywordsPolysubstance dependenceParaphernaliaAccidentalFentanylHeroinMedicineDrug overdosePoison controlEmergency medicineMedical emergencyAnesthesiaDrugSubstance abusePharmacologyPsychiatryGeography

Abstract

fetched live from OpenAlex

One of the quickest-growing subclasses of novel psychoactive substances is novel synthetic opioids (NSOs), which are categorized as fentanyl analogs (fentalogs) or nonfentanyl opioids that bind to the mu-opioid receptor. Increased detections of NSOs have been observed in the USA. However, limited information on their prevalence outside of the East Coast is available. This study details the prevalence of NSOs, specifically fluorofentanyl, in the biological and drug paraphernalia specimens of accidental overdose deaths in San Francisco in 2022. A recently developed and validated liquid chromatography with tandem mass spectrometry method was utilized for the analysis of >250 NSOs. Out of the 649 accidental overdose deaths in 2022, 617 cases were available for blood analysis, with at least 1 NSO detected in 48 cases (7.8%). Fentalogs were detected in all 48 cases, with fluorofentanyl being detected in 40 cases. In postmortem femoral blood, estimated concentrations of fluorofentanyl ranged from 0.1 to 8.9 ng/mL, and 0.05 to 85 ng/mL in urine. Polysubstance use with NSO was seen with fentanyl (89.6%), methamphetamine (70.8%), cocaine (33.3%), and heroin (18.8%). NSOs, mainly fluorofentanyl, were observed in matched drug paraphernalia. This report documents the migration of fluorofentanyl to the West Coast, specifically California.

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 score0.240
Threshold uncertainty score0.445

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 head0.017
GPT teacher head0.323
Teacher spread0.306 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

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