Ultralow-Dose Adjunctive Methadone with Slow Titration, Considering Long Half-Life, for Outpatients with Cancer-Related Pain
Bibliographic record
Abstract
Background: The unique properties of methadone make it attractive for use in cancer pain. The use of very low initial doses of adjunctive methadone is a promising strategy given its simplicity and potentially reduced risk profile. Objective: To understand if an ultralow-dose (ULD) methadone protocol (1 mg by mouth daily initial dose with gradual titration) can improve pain control in outpatients with cancer-related pain not responsive to previous opioids and/or nonopioid analgesics. We also sought to assess if the use of ULD methadone resulted in improvement in mood and sleep among other outcomes. Design and Setting/Subjects: This study is a retrospective chart review of outpatients at the cancer pain clinic at the Tom Baker Cancer Centre in Calgary, Alberta, Canada. Measurements: The mean ratings in maximum and average pain before methadone initiation, and at the final follow-up point are reported. Paired sample t tests evaluate for statistically significant differences in pain ratings before methadone initiation and at final follow-up. We also report the proportion of participants with a subjective improvement in pain, sleep, and mood (dichotomous “yes/no”), and the mean number of weeks to initial documented pain improvement. Results: 68.6% of patients (24/34) reported a subjective improvement in pain. Most patients reported improved sleep and mood (78.8% and 64.7%, respectively). Conclusions: More than two-thirds of patients reported an improvement in pain with a protocol using very low initial doses of adjunctive methadone. Our report is a preliminary retrospective chart review and larger prospective trials are warranted.
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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) | 0.000 | 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) | 0.001 | 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".