Postmortem <scp>THC</scp> in decedents following legalization of recreational cannabis in Clark County, Nevada
Bibliographic record
Abstract
Marijuana is the most prevalent illicit substance used globally. With increasing US legalizing recreational marijuana, more evidence is vital to minimize potential health risks. This study was conducted to test several hypotheses regarding postmortem THC/COOH-THC in decedents before and after legalization of recreational marijuana in Nevada. We also compared presence of THC/COOH-THC in decedents with respect to manner of death as recorded by the Clark County Office of the Coroner/Medical Examiner. THC/COOH-THC concentrations for years 2015-2016 (pre-legalization) and 2017-2019 (post-legalization) were compared through an independent samples t-test and chi-square tests. A binary logistic regression was used to compare the presence of THC/COOH-THC with covariates: age, gender, race, and manner of death. The average concentration of THC/COOH-THC detected per decedent did not significantly differ before and after recreational legalization, whereas the proportion of decedents testing positive for THC showed a small but significant increase following legalization although no significant change in COOH-THC was detected. The likelihood of testing positive for THC/COOH-THC decreased as age increased. Sex, race, and manner of death were all associated with the relative risk of presence of THC/COOH-THC in toxicology reports. An increase in proportion of users but not in concentration of THC/COOH-THC was observed after legalization. The results are generally consistent with national reports and suggest toxicology data from decedents is a valuable method for surveying marijuana use by the general public. The early adoption of recreational marijuana by neighboring states may have precluded any major changes in use following legalization in Nevada.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".