Bibliographic record
Abstract
Cannabinoids are often excluded from postmortem toxicology screens due to their ubiquitous nature, interpretative difficulties and unanswered questions regarding their postmortem redistribution. In this study, we review 30 postmortem cases where a drug screen gave a positive cannabinoids result and a confirmation identified Δ⁹-tetrahydrocannabinol (THC), 11-hydroxy-Δ⁹-tetrahydrocannabinol (11-OH-THC), and/or 11-nor-9-carboxy-Δ⁹-tetrahydrocannabinol (THC-COOH) in peripheral (BL-P) or cardiac/central blood (BL-C) and/or urine (UR). Had cannabinoids not been included in these toxicologic evaluations, incomplete or erroneous inferences would have been drawn in a substantial number of cases regarding cause/manner of death. THC was detected in 28 BL-C and in all 30 BL-P. THC and THC-COOH were confirmed present in 2 and 23 UR, respectively. 11-OH-THC was detected in 4 BL-C, 6 BL-P, and 0 UR. The mean THC concentrations in BL-C and BL-P were 8.0 and 15.8 ng/mL, respectively. The mean THC-COOH concentrations in BL-C and BL-P were 55.2 and 60.6 ng/mL, respectively. The mean 11-OH-THC concentrations in BL-C and BL-P were 17.0 and 12.5 ng/mL, respectively. Postmortem interval (PMI) for each case was determined and evaluated in relation to BL-C/BL-P concentration ratios with THC-COOH exhibiting a possible trend. This study is the first of its kind and demonstrates the usefulness of cannabinoid analyses as part of death investigations. Furthermore, it provides distribution data that will improve the ability of toxicologists and pathologists to evaluate cannabinoid concentrations in human postmortem specimens.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".