Rotating to Oral Methadone in Advanced Cancer Patients: A Case Series
Bibliographic record
Abstract
CONTEXT: Methadone is increasingly being used to treat patients whose pain does not respond well to other opioids. Advantages over morphine sulphate and its alternatives include low cost, lack of active metabolites and efficacy against neuropathic pain. OBJECTIVES: To describe our experience with opioid rotation to methadone and compare the morphine to methadone ratios to previously published data; To discuss two commonly used rotation methods--the Edmonton and Morley-Makin methods. METHOD: We describe two cases with cancer pain successfully switched to methadone. In both cases the dose of the previous opioid was limited by development of opioid toxicity. We used the Morley-Makin conversion method and modified it by reducing the 'as required' dose by a third. The initial methadone doses for these cases were lower than predicted doses. CONCLUSION: In cases where cancer patients fail to respond or develop tolerance to opioids, conversion to methadone is a reasonable approach. Although equianalgesic tables may not always predict final methadone doses, when properly selected can be useful tools for the experienced clinician. A customised and cautious approach is thus advisable when rotating to oral methadone, especially in patients who have experienced opioid toxicity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".