Non-Invasive Cytology Brush PCR Diagnostic Testing in Mucosal Leishmaniasis: Superior Performance to Conventional Biopsy with Histopathology
Bibliographic record
Abstract
BACKGROUND: Traditional methods of diagnosing mucosal leishmaniasis (ML), such as biopsy with histopathology, are insensitive and require collection of an invasive diagnostic specimen. METHODS: We compared standard invasive procedures including biopsy histopathology, biopsy PCR, and leishmanin skin test (LST) to a novel, non-invasive, cytology-brush based PCR for the diagnosis of ML in Lima, Peru. Consensus reference standard was 2/4 tests positive, and outcome measures were sensitivity and specificity. Leishmania species identification was performed by PCR-based assays of positive specimens. RESULTS: Twenty-eight patients were enrolled, 23 of whom fulfilled criteria for a diagnosis of ML. Sensitivity and specificity of biopsy with histopathology were 21.7% [95% CI 4.9-38.5%] and 100%; 69.6% [95% CI 50.8-88.4%] and 100% for LST; 95.7% [95% CI 87.4-100%] and 100% for biopsy PCR; and 95.7% [95% CI 87.4-100%] and 90% [95% CI 71.4-100%] for cytology brush PCR using both Cervisoft® and Histobrush® cervical cytology brushes. Represented species identified by PCR-RFLP included: L. (V). braziliensis (n = 4), and L. (V). peruviana (n = 3). CONCLUSIONS: Use of commercial grade cytology brush PCR for diagnosis of ML is sensitive, rapid, well tolerated, and carries none of the risks of invasive diagnostic procedures such as biopsy. Further optimization is required for adequate species identification. Further evaluation of this method in field and other settings is warranted.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".