Clinical and Demographic Stratification of Test Performance: A Pooled Analysis of Five Laboratory Diagnostic Methods for American Cutaneous Leishmaniasis
Bibliographic record
Abstract
We evaluated performance characteristics of five diagnostic methods for cutaneous leishmaniasis. Patients who came to the Leishmania Clinic of Hospital Nacional Cayetano Heredia in Lima, Peru, were enrolled in the study. Lesion smears, culture, microculture, polymerase chain reaction (PCR), and leishmanin skin test (LST) were performed. A total of 145 patients with 202 lesions were enrolled: 114 patients with 161 lesions fulfilled criteria for cutaneous leishmaniasis. Sensitivity and specificity were 57.8% (95% confidence interval [CI] = 50.2-65.4%) and 100.0% for culture, 78.3% (95% CI = 71.9-84.7%) and 100.0% for microculture, 71.4% (95% CI = 64.4-78.4%) and 100.0% for smears, 78.2% (95% CI = 70.6-85.8%) and 77.4% (95% CI = 62.7-92.1%) for LST, and 96.9% (95% CI = 94.2-99.6%) and 65.9% (95% CI = 51.4-80.4%) for PCR. PCR was more sensitive than the other assays (P < 0.001). Sensitivities of culture, smears, and LST varied by lesion duration and appearance. PCR offers performance advantages over other assays, irrespective of patient age, sex, lesion duration, or appearance. That clinical factors influence performance of non-molecular assays offers clinicians a patient-focused approach to diagnostic test selection.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".