Methadone-Nicotine Interactions in Methadone Maintenance Treatment Patients
Bibliographic record
Abstract
Smoking is highly prevalent (85%-98%) in methadone maintenance treatment (MMT) patients. Methadone has been shown to increase cigarette smoking in a dose-dependent manner, whereas smoking/nicotine has been shown to increase methadone self-administration and reinforcing properties. The objective of this study was to evaluate methadone-nicotine interactions in MMT patients during trough and peak methadone effect conditions. Subjective effects of nicotine (administered by cigarette smoking, 4 mg of nicotine gum and placebo gum) and methadone and their combination were assessed in 40 regularly smoking, stabilized MMT patients using a randomized, placebo-controlled, within-subject study design. Subjects responded to a battery of subjective assessments before and after nicotine administration both before methadone administration (cycles 1 and 2) and 3 hours after methadone administration (cycles 3 and 4). There was a main effect of methadone on the decrease of opioid withdrawal scores (P < 0.001), and cigarette smoking enhanced this effect (day x methadone interaction, P = 0.031). Both nicotine and methadone had main effects on the decrease of nicotine withdrawal scores (P < 0.001 and P = 0.001, respectively); this was associated with the cigarette day (day x nicotine interaction, P = 0.003, and day x methadone interaction, P = 0.004). Nicotine plasma levels were highest on the cigarette smoking day (P < 0.001). Methadone and nicotine shared main effects on the increase of ratings of euphoria and drug liking and on the decrease of restlessness, irritability, and depression. The overall results may help to explain high smoking rates in the MMT population and may account for reports of increased positive effects of methadone when the drugs are taken together.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".