Major Depressive Disorder and Patient Satisfaction in Relation to Methadone Pharmacokinetics and Pharmacodynamics in Stabilized Methadone Maintenance Patients
Bibliographic record
Abstract
Many patients enrolled in methadone maintenance treatment experience significant interdose opioid withdrawal. Mood states have been related to patient satisfaction with treatment and may influence how methadone patients experience opioid withdrawal. The objective of this study was to investigate the influence of major depressive disorder on response to methadone in patients on methadone maintenance treatment. Seventeen methadone patients (7 depressed and 10 not depressed) had pharmacokinetic and pharmacodynamic assessments (opioid withdrawal, drug effects, and mood) over one 24-hour dosing interval. Subjects were also divided based on their satisfaction with methadone treatment: 12 holders and 5 nonholders. Depressed subjects experienced more dysphoric opioid effects as measured by the Addiction Research Centre Inventory (area under the effect versus time curve, 14 +/- 32 vs -31 +/- 47, P < 0.04) and had higher scores on the Subjective Opioid Withdrawal Scale (area under the effect versus time curve, 33 +/- 97 vs -74 +/- 67, P < 0.02) over the dosage interval. Hamilton Depression scores significantly correlated with trough subjective opioid withdrawal scale scores (r = 0.7, P < 0.004). Nonholders had significantly higher exposure to unbound (S)-methadone compared with holders, specifically: trough concentration (6.1 +/- 2.7 ng/mL vs 2.7 +/- 1.7 ng/mL, P < 0.01), average steady-state concentration (7.6 +/- 4.0 ng/mL vs 4.1 +/- 2.5 ng/mL, P < 0.05), maximum concentration (14.6 +/- 7.1 ng/mL vs 7.5 +/- 4.2 ng/mL, P < 0.04), and area under the curve (183 +/- 95 h*ng/mL vs 99 +/- 61 h*ng/mL, P < 0.05). Study findings suggest that (S)-methadone may relate to patients' dissatisfaction with methadone treatment. Depressed methadone patients may be more sensitive to negative opioid effects and opioid withdrawal.
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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 | 0.003 |
| 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 | 0.001 | 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".