Addition of Methadone to Another Opioid in the Management of Moderate to Severe Cancer Pain: A Case Series
Bibliographic record
Abstract
BACKGROUND: Previous research has reported improved pain after adding methadone to another opioid, but did not quantify this benefit using a validated outcome measure. OBJECTIVE: To assess quantitatively the effectiveness of adding methadone to another opioid for moderate to severe cancer-related pain. DESIGN: All outpatients attending the Oncology Palliative Care Clinic from September 2010-September 2011, who had received methadone, were identified from pharmacy records. Inclusion criteria included: histological diagnosis of malignancy, age >18 years, taking regular opioids and Edmonton Symptom Assessment System (ESAS) pain score ≥ 4. MEASUREMENT: The primary outcome measure was a decrease in pain score of ≥ 2 points from methadone initiation to one-month follow-up (or closest available ESAS). RESULTS: Twenty patients were available for analysis, 16 of whom had neuropathic pain (80%). Eight patients (40%) had a decrease in pain score of ≥ 2 points at 1 month and a further 7 (35%) had a decrease of ≥ 2 points at the closest available time point. The mean pain score decreased from 7.7 +/- 1.8 to 5.2 +/- 2.4 from time of initiation to time of evaluation. The mean daily routine morphine equivalent, (excluding methadone), was 338 +/- 217.8 mg/day at initiation and 332 +/- 191 mg/day at evaluation; for methadone, mean doses at initiation and evaluation were 4.4 +/- 1.4 mg/day and 15.5 +/- 5.9 mg/day, respectively. Methadone was well tolerated in 17 patients (85%). CONCLUSIONS: The addition of methadone was associated with improved pain control for patients with moderate to severe pain on another opioid and appears to offer a safe, well-tolerated and practical alternative in this situation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".