Fatalities Associated with Fentanyl and Co‐administered Cocaine or Opiates
Bibliographic record
Abstract
Fatalities associated with fentanyl hydrochloride are increasingly seen in Massachusetts. Between September 2005 and November 2006, 5009 medicolegal investigations associated 107 deaths with licit or illicit fentanyl use, along with a co-detection of an opiate/opioid or cocaine/benzoylecognine, or both. Deaths associated with illicit fentanyl use occur in younger people (39.4 vs. 61.5 years) with higher fentanyl (17.1 ng/mL vs. 4.4 ng/mL) and lower morphine (76.9 ng/mL vs. 284.2 ng/mL) postmortem blood concentrations, and more frequent cocaine co-intoxication (65% vs. 3%), than deaths associated with illicit fentanyl use. A wide range of postmortem blood concentrations of fentanyl was detected (trace-280 ng/mL), with a minimum concentration of 7 ng/mL of fentanyl strongly associated with illicit use of fentanyl in poly-drug cases. The most commonly detected opiates/opioids in illicit fentanyl users were: morphine (29%), oxycodone (14.5%), and methadone (14.5%). Ethanol, cannabinoids, diazepam, citalopram, and diphenhydramine were each detected in greater than 10% of the licit fentanyl cases. Most fentanyl abusers died at their own home and their deaths were most often classified as accidental. Mapping of primary residences of decedents revealed conspicuous clustering of the illicit fentanyl use cases, as opposed to the random pattern in licit use cases. Fentanyl misuse is a public health problem in Massachusetts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".