Safety and Efficacy of Rapid Methadone Titration for Opioid Use Disorder in an Inpatient Setting: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: Inpatient guidelines for methadone titration do not exist, whereas outpatient guidelines lack flexibility and do not consider individual opioid tolerance. The evaluation of rapid, adaptable titration protocols may allow more patient-centered and effective treatment for opioid use disorder in the fentanyl era. METHODS: This study performed a retrospective chart review of patients 18 years or older with opioid use disorder who were initiated on methadone at a single academic urban hospital using a rapid divided dose protocol between November 2019 and November 2020. The primary outcome was adverse events associated with methadone, specifically opioid toxicity or sedation requiring increased medical observation or intervention. The secondary outcome was total daily dose of methadone received on day 7 of titration. RESULTS: Ninety-eight patients were included for a total of 168 visits. Sixty-five (66%) were male, with a median age of 38 years (interquartile range, 31-42 years). Sedation occurred in 2 patients (1%), who required either naloxone administration or transfer to an intensive care unit for monitoring. Of the 135 visits where patients received at least 7 days of methadone, the mean dose on day 1 was 41 mg (SD, 9.6 mg) and on day 7 was 65 mg (SD, 20.9 mg). CONCLUSIONS: In this inpatient cohort, rapid methadone titration was well tolerated and resulted in patients reaching higher doses of methadone than would be possible with a standard schedule, with few adverse events. Given the known effective dose range, this approach may result in shorter time to clinical stabilization and suggests that alternative methadone titration schedules may be safe and effective in appropriately selected patients.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".