Combination therapy with methadone and duloxetine for cancer-related pain: a retrospective study
Bibliographic record
Abstract
BACKGROUND: A comprehensive approach to pain management often requires multimodal therapy and a combination of medications. Oncology patients may be prescribed methadone and duloxetine as single agents or in combination for cancer-related pain, particularly neuropathic pain. Duloxetine is also prescribed for depression or anxiety in patients with cancer. METHODS: A retrospective chart review on patients with cancer-related pain prescribed duloxetine and methadone combination therapy at the Virginia Commonwealth University supportive care clinic (SCC) between 2012 and 2019. Edmonton Symptom Assessment System (ESAS) scores reported by patients on monotherapy were compared to scores after they started combination therapy. Of 131 patients identified on combination therapy, 43 met study criteria (2 with incomplete ESAS scores). RESULTS: ESAS total and subscores after combination therapy were lower than on monotherapy. Combination therapy decreased total, pain, and emotion subscores by 5.6 (SD =17.3, dz =-0.32, P=0.046), 0.9 (SD =3.0, dz =-0.30, P=0.052), and 1.8 (SD =5.1, dz =-0.36, P=0.023), respectively. On combination therapy, 28% of patients reported at least a two-point reduction in pain scores. All study participants reported cancer pain with neuropathic components; most had mixed pain syndromes comprising nociceptive and neuropathic components. Adherence rates were high as 81% of patients with follow-up appointments continued therapy. CONCLUSIONS: These results suggest the combination of duloxetine and methadone reduces cancer-related pain and emotional symptom burden compared to either medication as a single agent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".