Use of Essential Medicines for Pain Relief and Palliative Care: A Global Consensus Process
Bibliographic record
Abstract
CONTEXT: The WHO Model List of Essential Medicines includes 24 medications under the section Medicines for Pain and Palliative Care (EML). The Lancet Commission on Pain and Palliative Care developed the Lancet Essential Package (LEP), including 35 medications designed to alleviate serious health-related suffering worldwide. OBJECTIVES: This study aims to provide recommendations on the appropriate use of essential of medicines in palliative care. METHODS: The global palliative care community was invited to submit guidelines, of which 19/22 were selected. Data was extracted on initial dose, frequency, and maximum daily dose for medications in the LEP and in the WHO EML. For medications where guidance was not available or information differed, a 2-round Delphi process was conducted with 70 experts across regions and income levels. Consensus was set to ≥70% agreement. RESULTS: Consensus in the guidelines was identified for 24 medications on three parameters. Open questions (mostly on maximum daily dose) were included in the Delphi. 63 experts from 49 countries responded (RR = 90%). No consensus was achieved for the maximum daily dose for nine medications. Significant disparities in medication availability were noted between high-income and low/middle-income countries. CONCLUSION: We were able to partly achieve our goal, with limited evidence and a wide range of clinical practice described by the experts. This highlights an important gap in critical information which affects mostly the provision of palliative care at the primary care. Both limited availability and lack of training on the adequate use of essential medications may affect how clinicians manage symptoms, possibly relying on personal experience or trial and error, rather than evidence-based information.
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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 | 0.370 | 0.294 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".