Barriers to treatment for visceral leishmaniasis in hyperendemic areas: India, Bangladesh, Nepal, Brazil and Sudan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CONTEXT: Visceral leishmaniasis (VL) is a severe and potentially fatal infection caused by the trypanosome parasite Leishmania sp. Over 90% of reported cases occur in India, Bangladesh, Nepal, Sudan, and Brazil, affecting mainly impoverished individuals and creating a significant economic burden through direct and indirect costs of treatment. OBJECTIVES: To identify the direct and indirect costs of VL treatment, compare these costs to household income, and identify the barriers to treatment in each of the five VL-endemic countries. METHODS: Articles obtained through PubMed (US National Library of Medicine), EMBASE, and Cochrane Library were selected for relevance to VL treatment, costs for all forms of amphotericin B, miltefosine, paromomycin, and antimony compounds, and healthcare costs in India, Bangladesh, Nepal, Brazil, and Sudan. Healthcare statistics were obtained from the World Health Organization Statistical Information System, Médecins Sans Frontieres, and each country's national health ministry. RESULTS: Per capita GDP, per capita GNI, cost of drugs, and hospitalization expenses differ by up to 10-fold in each of the five countries where VL is hyperendemic, resulting in unequal barriers to treatment. We found that the cost of specific drugs influences the choice of therapy. CONCLUSIONS: Poverty and VL treatment-related costs cause potential limitations in the provision of full and efficacious treatment, which may result in further dissemination of the disease. Effective nonparenteral antileishmania drugs would provide a significant advantage in reducing the barriers to VL treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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