Atypical cutaneous leishmaniasis: a new challenge to VL elimination in South-East Asia
Bibliographic record
Abstract
Visceral leishmaniasis (VL) caused by L. donovani in South-East Asian endemic countries including India, Nepal and Bangladesh has been the primary focus of the ongoing VL elimination program. With a major reduction in VL cases resulting from the elimination program during the last two decades, the efforts are now focused on the challenges posed by potential reservoirs within the asymptomatic cases, HIV-co-infection VL cases and Post Kala-azar Dermal Leishmaniasis (PKDL) cases that continue to sustain the parasite transmission cycle in known and newer endemic zones. This article brings attention to a new potential parasite reservoir in the form of atypical cutaneous leishmaniasis (ACL) cases caused by novel L. donovani genetic variants. L. donovani mediated ACL is an emerging phenomenon in recent endemic sites that now justify a need for implementing molecular surveillance tools to identify region-specific L. donovani variants with dermotropic capabilities and potential to revert to visceral disease. A timely detection of novel ACL causing L. donovani genetic lineages in South-East Asian endemic regions is necessary to halt the spread of ACL and is potentially crucial for the sustainability of the advances made by the VL elimination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".