Using GRADE methodology for the development of public health guidelines for the prevention and treatment of HIV and other STIs among men who have sex with men and transgender people
Bibliographic record
Abstract
The World Health Organization (WHO) Department of HIV/AIDS led the development of public health guidelines for delivering an evidence-based, essential package of interventions for the prevention and treatment of HIV and other sexually transmitted infections (STIs) among men who have sex with men (MSM) and transgender people in the health sector in low- and middle-income countries. The objective of this paper is to review the methodological challenges faced and solutions applied during the development of the guidelines. The development of the guidelines followed the WHO guideline development process, which utilizes the GRADE approach. We identified, categorized and labeled the challenges identified in the guidelines development process and described the solutions through an interactive process of in-person and electronic communication. We describe how we dealt with the following challenges: (1) heterogeneous and complex interventions; (2) paucity of trial data; (3) selecting outcomes of interest; (4) using indirect evidence; (5) integrating values and preferences; (6) considering resource use; (7) addressing social and legal barriers; (8) wording of recommendations; and (9) developing global guidelines. We were able to successfully apply the GRADE approach for developing recommendations for public health interventions. Applying the general principles of the approach while carefully considering specific challenges can enhance both the process and the outcome of guideline development.
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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 | 0.289 | 0.515 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.037 | 0.023 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".