Integrating Case Detection of Visceral Leishmaniasis and Other Febrile Illness with Vector Control in the Post-Elimination Phase in Nepal
Why this work is in the frame
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Bibliographic record
Abstract
Nepal has completed the attack phase of visceral leishmaniasis (VL) elimination and now needs active case detection (ACD) and vector control methods that are suitable to the consolidation and maintenance phases. We evaluated different ACD approaches and vector control methods in Saptari district. We assessed 1) mobile teams deployed in villages with VL cases in 2015 to conduct combined camps for fever and skin lesions to detect VL/PKDL (post-kala-azar dermal leishmaniasis) and other infections; 2) an incentive approach by trained female community health volunteers (FCHVs) in villages with no VL cases in 2015. Both were followed by house-to-house visits. For vector control, four villages were randomly allocated to insecticide impregnation of bednets, insecticide wall painting, indoor residual spraying (IRS), and control. Sandfly density (by CDC light traps, The John W. Hock Company, USA) and mortality (World Health Organization cone bioassay) were assessed in randomly selected households. One VL, three tuberculosis, one leprosy, and one malaria cases were identified among 395 febrile cases attending the camps. Post-camp house-to-house screening involving 7,211 households identified 679 chronic fever and 461 skin lesion cases but no additional VL/PKDL. No VL/PKDL case was found by FCHVs. The point prevalence of chronic fever in camp and FCHV villages was 242 and 2 per 10,000 populations, respectively. Indoor residual spraying and bednet impregnation were effective for 1 month versus 12 months with insecticidal wall paint. Twelve-month sandfly mortality was 23%, 26%, and 80%, respectively, on IRS, bednet impregnation, and insecticide wall painting. In Nepal, fever camps and insecticidal wall paint prove to be alternative, sustainable strategies in the VL post-elimination program.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it