Accelerating the Uptake of WHO Recommendations for Mass Drug Administration Using Ivermectin, Diethylcarbamazine, and Albendazole
Bibliographic record
Abstract
Triple therapy with ivermectin, diethylcarbamazine, and albendazole (IDA) for the elimination of lymphatic filariasis (LF) represents a compelling example of accelerating the timeline from development to introduction and impact. Previous articles outlined how the clinical development process was able to compress timelines and provide the evidence needed for the WHO to issue guidelines on the use of IDA for mass drug administration for LF. We explored the drivers for the rapid and successful introduction of IDA in the early-adopter countries. Lessons from this experience highlight five key elements for moving from WHO recommendations to program uptake after the publication of the guideline: 1) early engagement with stakeholders to create partnerships to coordinate and plan for implementation; 2) recognition by countries and partners of the potential of IDA to improve efforts to eliminate LF; 3) high-level commitment and coordination at regional levels and, most importantly, at the country level; 4) understanding of the perspectives among people living in LF-endemic communities where mass drug administration is warranted; and 5) affirmation of the feasibility of IDA through sharing lessons learned.
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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 | 0.040 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".