Fluorofentanyl and novel synthetic opioids in accidental overdose deaths
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".