Methadone Toxicity Fatalities: A Review of Medical Examiner Cases in a Large Metropolitan Area*
Bibliographic record
Abstract
Over the past several years, Medical Examiners in Kentucky and around the nation have observed a dramatic rise in drug intoxication deaths involving the prescription medication methadone. This documented rise in methadone-related deaths requires a better understanding of methadone's pathophysiology and the ways it contributes to significantly increase morbidity and mortality. This study reviews 176 fatalities ascribed to methadone toxicity by the Office of the Chief Medical Examiner in Kentucky between 2000 and 2004. Postmortem toxicological analysis recorded a more than 10-fold increase in methadone toxicity fatalities, rising from 6 cases in 2000 to 68 cases in 2003. Of the 176 methadone-related fatalities, methadone was the only drug detected in postmortem blood and urine toxicological analyses in 11 (6.25%) cases. The mean methadone blood concentration of all 176 cases was 0.535 mg / L (0.02-4.0). The following psychoactive medications were detected: antidepressants (39.8%), benzodiazepines (32.4%), and other opioids in addition to methadone (27.8%). Cannabinoids were detected in 44 (28.4%) cases and cocaine or metabolite in 34 (21.9%) cases. Of the 95 cases with a known history of methadone use, 46 (48.4%) involved prescription by private physician. The interpretation of blood methadone concentrations alone or combined with other psychoactive drugs requires consideration of the subject's potential chronic use of and tolerance to the drug. A thorough investigation into the practices of procurement and use/abuse of methadone is essential to arrive at the proper designation of the cause of death.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".