Comparison of the Diagnostic Performances of Five Different Tests in Diagnosing Visceral Leishmaniasis in an Endemic Region of Ethiopia
Bibliographic record
Abstract
The lack of accurate and feasible diagnostic tests poses a significant challenge to visceral leishmaniasis (VL) healthcare services in endemic areas. To date, various VL diagnostic tests have been or are being developed, and their diagnostic performances need to be assessed. In the present study, the diagnostic performances of rk39 RDT, the direct agglutination test (DAT), microscopy, loop-mediated isothermal amplification (LAMP), and miniature direct-on-blood polymerase chain reaction-nucleic acid lateral flow immunoassay (mini-dbPCR-NALFIA) were assessed using quantitative polymerase chain reaction (qPCR) as the reference test in an endemic region of Ethiopia. In this study, 235 suspected VL cases and 104 non-endemic healthy controls (NEHCs) were recruited. Among the suspected VL cases, 144 (61.28%) tested positive with qPCR. The sensitivities for rk39 RDT, DAT, microscopy, LAMP assay, and mini-dbPCR-NALFIA were 88.11%, 96.50%, 76.58%, 94.33%, and 95.80%, respectively. The specificities were 83.33%, 97.96%, 100%, 97.38%, and 98.92% for rk39 RDT, DAT, microscopy, LAMP assay, and mini-dbPCR-NALFIA, respectively. In conclusion, rk39 RDT and microscopy exhibited lower sensitivities, while DAT demonstrated excellent performance. LAMP and mini-dbPCR-NALFIA showed excellent performances with feasibility for implementation in remote endemic areas, although the latter requires further evaluation in such regions.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".