Mortality among individuals accessing pharmacological treatment for opioid dependence in California, 2006–10
Bibliographic record
Abstract
AIMS: To estimate mortality rates among treated opioid-dependent individuals by cause and in relation to the general population, and to estimate the instantaneous effects of opioid detoxification and maintenance treatment (MMT) on the hazard of all-cause and cause-specific mortality. DESIGN: Population-based treatment cohort study. SETTING: Linked mortality data on all individuals first enrolled in publicly funded pharmacological treatment for opioid dependence in California, USA from 2006 to 2010. PARTICIPANTS: A total of 32 322 individuals, among whom there were 1031 deaths (3.2%) over a median follow-up of 2.6 years (interquartile range = 1.4-3.7). MEASUREMENTS: The primary outcome was mortality, indicated by time to death, crude mortality rates (CMR) and standardized mortality ratios (SMR). FINDINGS: Individuals being treated for opioid dependence had a more than fourfold increase of mortality risk compared with the general population [SMR = 4.5, 95% confidence interval (CI) = 4.2, 4.8]. Mortality risk was higher (1) when individuals were out-of-treatment (SMR = 6.1, 95% CI = 5.7, 6.5) than in-treatment (SMR = 1.8, 95% CI = 1.6, 2.1) and (2) during detoxification (SMR = 2.4, 95% CI = 1.5, 3.8) than during MMT (SMR = 1.8, 95% CI = 1.5, 2.1), especially in the 2 weeks post-treatment entry (SMR = 5.5, 95% CI = 2.7, 9.8 versus SMR = 2.5, 95% CI = 1.7, 4.9). Detoxification and MMT both independently reduced the instantaneous hazard of all-cause and drug-related mortality. MMT preceded by detoxification was associated with lower all-cause and other cause-specific mortality than MMT alone. CONCLUSIONS: In people with opiate dependence, detoxification and methadone maintenance treatment both independently reduce the instantaneous hazard of all-cause and drug-related mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".