Methadone in Cancer-Related Neuropathic Pain: A Narrative Review
Bibliographic record
Abstract
Background and Objective: Cancer-related neuropathic pain (CRNP) is often a significant burden on patients’ quality of life. There are limited treatment guidelines for cancer-related neuropathic pain outside of CIPN. Although opioids are considered a third-line treatment option, no consensus exists on which opioid is most effective, either as a single agent or in combination with other medications. Our aim is to review and update the literature for methadone use in CRNP, since the last review was conducted in 2006. Methods: A comprehensive literature search was performed to evaluate the use of methadone in cancer-related neuropathic pain. Articles were identified from PubMed, Google Scholar, and Cochrane Library using the following keywords: “methadone AND cancer pain AND neuropathic pain” and “cancer-related opioid treatment”. Results: Studies were included if they evaluated methadone’s efficacy or safety in neuropathic pain management for patients with cancer. This review focused on randomized controlled trials (RCTs), systematic reviews, meta-analyses, and observational studies published between 2000 and 2024. Studies were excluded if they lacked specific data on cancer-related neuropathic pain or were case reports. Conclusions: The unique mechanisms of action and preliminary clinical trials support methadone’s status as the first opioid to consider for CRNP when non-opioid first-line treatments have failed to alleviate patient symptoms. Methadone can also be considered as a first-line opioid in patients with mixed nociceptive–neuropathic pain and any of the following features: renal dysfunction; administration of opioids through a feeding tube; a lack of financial resources/insurance; and a switch from another high-dose opioid. More research is needed regarding methadone for CRNP and methadone’s preferential use in specific sub-groups of patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".