Using GRADE to develop the WHO guideline on verifying elimination of human onchocerciasis
Bibliographic record
Abstract
Issue The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was initially developed to support guideline development for therapeutic decisions and recently was expanded to address diagnostic questions. We used GRADE for a guideline on the decision to stop mass drug administration (MDA) and to verify elimination of a disease. This is this first documented use of GRADE for this type of guideline. Problem Human onchocerciasis is caused by the parasitic worm Onchocerca volvulus and causes skin disease and “river blindness”. Several previously endemic countries have implemented MDA with ivermectin and successfully achieved elimination. Recently, the World Health Organization (WHO) updated its 2001 guidelines for the verification of elimination of onchocerciasis. We developed an analytic framework to describe the pathway from MDA to surveillance and verification of elimination of onchocerciasis. We systematically searched for published and unpublished studies and constructed a “linked evidence” chain. We combined evidence from diagnostic accuracy and observational studies and judged the certainty of the evidence using the applicable GRADE method. We then developed GRADE decision tables to summarize all the evidence for benefits and harms, cost, feasibility, equity, and acceptability. Effects In a face-to-face meeting, the guideline panel used the decision tables to make either strong or conditional recommendations for or against each test under consideration. Where available evidence was of very low certainty the panel members relied on their personal knowledge of data that were not publically available (e.g., internal government or WHO field office reports). Lessons Using an analytic framework and GRADE allowed us to present diverse evidence to the guideline panel in a structured manner; however personal knowledge of programme data played a role in panel decisions although it was not captured in the GRADE evidence summaries. Key messages: Providing evidence support for a WHO guideline on elimination of a disease required us to construct an analytic framework and combine multiple study types in a linked evidence chain The role of regular programme generated data (cf published studies) in informing WHO guidelines developed using the GRADE approach should be explored/defined
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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 | 0.138 | 0.388 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.025 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".