Methadone as a Coanalgesic for Palliative Care Cancer Patients
Bibliographic record
Abstract
BACKGROUND: Methadone offers many advantages for treating cancer pain. However, its pharmacokinetic profile makes its use as a full-dose opioid challenging. OBJECTIVES: To evaluate the efficacy and safety of low-dose methadone as an adjunct to opioids in the treatment of cancer pain in palliative care patients. DESIGN: A cohort was followed retrospectively for up to 60 days after the initiation of methadone as a coanalgesic. SETTING/SUBJECTS: Patients were eligible if they were prescribed methadone as a coanalgesic for cancer pain management and followed by the palliative care team. MEASUREMENTS: The primary efficacy end point was reduction of pain intensity (11-point numerical rating scale). Variables associated with pain intensity reduction were explored using logistic regressions. Adverse events were collected throughout the follow-up. RESULTS: Seventy-two of the 146 subjects (49%) qualified as significant responders (≥30% reduction in pain intensity). Median time to significant response was seven days, and pain intensity on the day of methadone initiation predicted the response to treatment. The most frequently reported adverse events were drowsiness, confusion, constipation, and nausea. As expected in a palliative care population, there was a substantial amount of missing data. CONCLUSIONS: A significant reduction in pain can be seen rapidly after the addition of methadone as a coanalgesic, particularly among patients with high pain intensity. More studies are needed to corroborate the efficacy of methadone as an adjunct to opioids.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".