A Research Agenda for Helminth Diseases of Humans: Towards Control and Elimination
Bibliographic record
Abstract
Human helminthiases are of considerable public health importance in sub-Saharan Africa, Asia, and Latin America. The acknowledgement of the disease burden due to helminth infections, the availability of donated or affordable drugs that are mostly safe and moderately efficacious, and the implementation of viable mass drug administration (MDA) interventions have prompted the establishment of various large-scale control and elimination programmes. These programmes have benefited from improved epidemiological mapping of the infections, better understanding of the scope and limitations of currently available diagnostics and of the relationship between infection and morbidity, feasibility of community-directed or school-based interventions, and advances in the design of monitoring and evaluation (M&E) protocols. Considerable success has been achieved in reducing morbidity or suppressing transmission in a number of settings, whilst challenges remain in many others. Some of the obstacles include the lack of diagnostic tools appropriate to the changing requirements of ongoing interventions and elimination settings; the reliance on a handful of drugs about which not enough is known regarding modes of action, modes of resistance, and optimal dosage singly or in combination; the difficulties in sustaining adequate coverage and compliance in prolonged and/or integrated programmes; an incomplete understanding of the social, behavioural, and environmental determinants of infection; and last, but not least, very little investment in research and development (R&D). The Disease Reference Group on Helminth Infections (DRG4), established in 2009 by the Special Programme for Research and Training in Tropical Diseases (TDR), was given the mandate to undertake a comprehensive review of recent advances in helminthiases research, identify research gaps, and rank priorities for an R&D agenda for the control and elimination of these infections. This review presents the processes undertaken to identify and rank ten top research priorities; discusses the implications of realising these priorities in terms of their potential for improving global health and achieving the Millennium Development Goals (MDGs); outlines salient research funding needs; and introduces the series of reviews that follow in this PLoS Neglected Tropical Diseases collection, "A Research Agenda for Helminth Diseases of Humans."
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.015 | 0.030 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.028 | 0.024 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".