Challenges of using methadone in the Indian pain and palliative care practice
Bibliographic record
Abstract
Palliative care providers across India lobbied to gain access to methadone for pain relief and this has finally been achieved. Palliative care activists will count on the numerous strengths for introducing methadone in India, including the various national and state government initiatives that have been introduced recognizing the importance of palliative care as a specialty in addition to improving opioid accessibility and training. Adding to the support are the Non-Governmental Organizations (NGOs), the medical fraternity and the international interactive and innovative programs such as the Project Extension for Community Health Outcome. As compelling as the need for methadone is, many challenges await. This article outlines the challenges of procuring methadone and also discusses the challenges specific to methadone. Balancing the availability and diversion in a setting of opioid phobia, implementing the amended laws to improve availability and accessibility in a country with diverse health-care practices are the major challenges in implementing methadone for relief of pain. The unique pharmacology of the drug requires meticulous patient selection, vigilant monitoring, and excellent communication and collaboration with a multidisciplinary team and caregivers. The psychological acceptance of the patient, the professional training of the team and the place where care is provided are also challenges which need to be overcome. These challenges could well be the catalyst for a more diligent and vigilant approach to opioid prescribing practices. Start low, go slow could well be the way forward with caregiver education to prescribe methadone safely in the Indian palliative care setting.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".