<i>Leishmania</i> OligoC-TesT as a Simple, Rapid, and Standardized Tool for Molecular Diagnosis of Cutaneous Leishmaniasis in Peru
Bibliographic record
Abstract
Molecular methods such as PCR have become attractive tools for diagnosis of cutaneous leishmaniasis (CL), both for their high sensitivity and for their specificity. However, their practical use in routine diagnosis is limited due to the infrastructural requirements and the lack of any standardization. Recently, a simplified and standardized PCR format for molecular detection of Leishmania was developed. The Leishmania OligoC-TesT is based on simple and rapid detection using a dipstick with PCR-amplified Leishmania DNA. In this study, we estimated the diagnostic accuracy of the Leishmania OligoC-TesT for 61 specimens from 44 CL-suspected patients presenting at the leishmaniasis clinic of the Instituto de Medicina Tropical Alexander von Humboldt, Peru. On the basis of parasitological detection and the leishmanin skin test (LST), patients were classified as (i) confirmed CL cases, (ii) LST-positive cases, and (iii) LST-negative cases. The sensitivities of the Leishmania OligoC-TesT was 74% (95% confidence interval (CI), 60.5% to 84.1%) for lesion aspirates and 92% (95% CI, 81.2% to 96.9%) for scrapings. A significantly higher sensitivity was observed with a conventional PCR targeting the kinetoplast DNA on the aspirates (94%) (P = 0.001), while there was no significant difference in sensitivity for the lesion scrapings (88%) (P = 0.317). In addition, the Leishmania OligoC-TesT was evaluated for 13 CL-suspected patients in two different peripheral health centers in the central jungle of Peru. Our findings clearly indicate the high accuracy of the Leishmania OligoC-TesT for lesion scrapings for simple and rapid molecular diagnosis of CL in Peru.
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How this classification was reachedexpand
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".