GRADE Concept 7: Issues and Insights Linking Guideline Recommendations to Trustworthy Essential Medicine Lists
Bibliographic record
Abstract
OBJECTIVES: Guidelines and essential medicine lists (EMLs) bear similarities and differences in the process that lead to decisions. Access to essential medicines is central to achieve universal health coverage. The World Health Organization (WHO) EML has guided prioritization of essential medicines globally for nearly 50 years, and national EMLs (NEMLs) exist in over 130 countries. Guideline and EML decisions, at WHO or national levels, are not always coordinated and aligned. We sought to explore challenges, and potential solutions, for decision-making to support trustworthy medicine selection for EMLs from a Grading of Recommendations, Assessment, Development and Evaluations (GRADE) Working Group perspective. We primarily focus on the WHO EML; however, our findings may be applicable to NEML decisions as well. STUDY DESIGN AND SETTING: We identified key challenges in connecting the EML to health guidelines by involving a broad group of stakeholders and assessing case studies including real applications to the WHO EML, South Africa NEML, and a multiple sclerosis guideline connected to a WHO EML application for multiple sclerosis treatments. To address challenges, we utilized the results of a survey and feedback from the stakeholders, and iteratively met as a project group. We drafted a conceptual framework of challenges and potential solutions. We presented a summary of the results for feedback to all attendees of the GRADE Working Group meetings in November 2022 (approximately 120 people) and in May 2023 (approximately 100 people) before finalizing the framework. RESULTS: We prioritized issues and insights/solutions that addressed the connections between the EML and health guidelines. Our suggested solutions include early planning alignment of guideline groups and EMLs, considering shared participation to strengthen linkage, further clarity on price/cost considerations, and using explicit shared criteria to make guideline and EML decisions. We also provide recommendations to strengthen the connection between WHO EML and NEMLs including through contextualization methods. CONCLUSION: This GRADE concept article, jointly developed by key stakeholders from the guidelines and EMLs field, identified key conceptual issues and potential solutions to support the continued advancement of trustworthy EMLs. Adopting structured decision criteria that can be linked to guideline recommendations bears the potential to advance health equity and gaps in availability of essential medicines within and between countries.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.351 | 0.573 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.038 | 0.036 |
| Open science | 0.010 | 0.023 |
| Research integrity | 0.015 | 0.017 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".