Management of Chronic Neuropathic Pain With Methadone: A Review of 13 Cases
Bibliographic record
Abstract
The synthetic opioid methadone has generated much interest in recent years among clinicians involved in the management of intractable chronic cancer pain. Its use as an analgesic is starting to extend to the treatment of noncancer pain, particularly neuropathic pain. Unfortunately, the evidence for its use in the management of neuropathic pain is limited to a few case studies. We examined retrospectively during a 12-month study period the clinical response of all 13 patients at our pain clinic who were prescribed methadone in an attempt to control neuropathic pain resistant to conventional analgesics. A questionnaire was also administered to the 9 patients who continued to take methadone at 12 months posttreatment. A total of 4 patients (31%) discontinued it by the end of the 12-month study period. Patients discontinued methadone due to the absence of pain relief and due to various intractable, undesirable side effects. Somnolence was the most common adverse effect reported, followed by nausea, constipation, and vomiting. All patients took coanalgesics (eg, amitriptyline, gabapentin) or other analgesics (eg, morphine, nonsteroidal anti-inflammatory drugs) during methadone treatment to control pain. The 9 patients who continued to take methadone at 12 months reported experiencing on average 43% pain relief (range 0-80%), 47% improvement in quality of life (range 0-100%), and 30% improvement in quality of sleep (range 0-60%). Methadone was effective at relieving pain and ameliorating quality of life and sleep in 62% of patients. These findings suggest that methadone can offer an acceptable success rate for the treatment of neuropathic pain. Prospective randomized, placebo-controlled studies are now needed to examine more rigorously the benefits of methadone for this type of pain.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".