The use of methadone in adult patients with cancer pain at a governmental cancer center in India
Bibliographic record
Abstract
BACKGROUND: Management of cancer-related pain relies on the access to opioids. When regular opioids as morphine are not tolerated or are insufficient, adjuvant opioids as methadone are an affordable and effective analgesic. AIM: The aim of the project was to describe the pattern of use and clinical experiences of methadone in patients with cancer-related pain at a low-resource hospital in Hyderabad, one of few Indian cancer centers with permission to prescribe methadone. METHODS: Medical records of all patients who had been prescribed methadone, September 9, 2017 and November 19, 2019 were studied retrospectively. Data on analgesic treatment and opioid side effects were analyzed. RESULTS: A total of 93 adult cancer patients were included in the study. A majority of patients (79%) were prescribed opioid analgesic, mainly morphine, before methadone introduction. The initial daily dose of methadone ranged between 5 and 22.5 years and in the vast majority of the patients 5 mg, divided in two daily administrations. A good analgesic effect, with decreased pain, was reported in 60% of the patients. No severe side effects were reported. CONCLUSIONS: In this study, methadone as a primary opioid was used with a good analgesic effect for cancer pain in a low-resource setting. Indication for methadone was mainly uncontrolled pain with a regular opioid treatment. No severe adverse effects were reported. Further research and prospective studies are needed on methadone treatment in low-resource settings to establish the robust guidelines to support prescribing physicians.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".