Bibliographic record
Abstract
Background and Aims Adequate analgesia can be challenging, as pharmacological options are not necessarily effective for all types of pain and have various side effects. Methadone is increasingly being considered in the management of both cancer- and non-cancer-related pain. Objective: To summarize the evidence on the effectiveness of methadone and review the side effects and cost of this drug. Methods PubMed, Medline, Embase, and Google Scholar databases were searched to identify Randomized Controlled Trials (RCTs) assessing methadone and a comparison drug. Results A total of 40 RCTs were included. The majority compared methadone to morphine or fentanyl. Methadone was effective in certain orthopedic, spinal, and cardiac surgeries. It was superior to fentanyl in the management of head-and-neck cancer pain. There was variability in the limited data on the management of neuropathic pain. Side effects experienced with methadone use were similar to a comparison drug. The effectiveness of methadone in the management of post-surgical and cancer pain was dependent on the procedure and cancer type, respectively. Methadone may be useful as an adjunctive analgesic, to lower the dose of another drug Conclusions Methadone may be a valuable in the management of post-surgical, cancer, or nociceptive pain, and in patients with renal impairment. Prescribers should consult a specialist prior to starting or discontinuing methadone. Future Research: Reported outcomes for measuring analgesia must be standardized. Patients should be stratified by procedure and cancer type in future RCTs.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".