Sustainability of vector control strategies in the Gran Chaco Region: current challenges and possible approaches
Bibliographic record
Abstract
Sustainability has become a focal point of the international agenda. At the heart of its range of distribution in the Gran Chaco Region, the elimination of Triatoma infestans has failed, even in areas subject to intensive professional vector control efforts. Chagas disease control programs traditionally have been composed of two divorced entities: a vector control program in charge of routine field operations (bug detection and insecticide spraying) and a disease control program in charge of screening blood donors, diagnosis, etiologic treatment and providing medical care to chronic patients. The challenge of sustainable suppression of bug infestation and Trypanosoma cruzi transmission can be met through integrated disease management, in which vector control is combined with active case detection and treatment to increase impact, cost-effectiveness and public acceptance in resource-limited settings. Multi-stakeholder involvement may add sustainability and resilience to the surveillance system. Chagas vector control and disease management must remain a regional effort within the frame of sustainable development rather than being viewed exclusively as a matter of health pertinent to the health sector. Sustained and continuous coordination between governments, agencies, control programs, academia and the affected communities is critical.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".