Methadone for Pain Management in Chemotherapy-Induced Peripheral Neuropathy: A Retrospective Review
Bibliographic record
Abstract
Chemotherapy-Induced Peripheral Neuropathy (CIPN) refers to damage of peripheral nerve fibers due to the use of neurotoxic chemotherapy to treat various cancers. It occurs in more than 30% of patients and only duloxetine has currently been identified to show limited efficacy in symptomatic treatment of CIPN. Opioids have traditionally been used to treat cancer pain, and there is evidence for their use in treatment of peripheral neuropathic pain from other causes. With a similar mechanism of action to duloxetine, methadone has rationale for treating neuropathic pain. This study is a retrospective chart review to evaluate the outcomes of using methadone for CIPN pain. Out of 31 patients, 65% felt that methadone was an effective treatment, 19% felt that it was ineffective, and 16% felt that it was partially or temporarily effective. These results suggest that analgesic response to methadone varies between patients, but that it has a potential role in painful CIPN. Its advantages for long-term use include low cost and lack of metabolites. Potential risks include a long half-life, drug interactions, and potential for QT prolongation at high doses. Prospective studies should be conducted to evaluate the role of methadone in CIPN pain management more comprehensively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".