Leishmania species and clinical characteristics of Pacific and Amazon cutaneous leishmaniasis in Ecuador and determinants of health-seeking delay: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Cutaneous Leishmaniasis (CL) affects up to 5.000 people in Ecuador each year. L. guyanensis and L. braziliensis are the most common of the eight CL-causing Leishmania species. Earlier CL research concentrated on the easily accessible Pacific region. This study aims to describe the Leishmania species in Pacific and Amazon ecoregions, to analyze regional differences in CL patient clinical presentation, and to identify determinants of health-seeking delay. METHODS: All cases in this cross-sectional study were diagnosed using smear slide microscopy, PCR, or both. Cytochrome B gene sequencing was used to identify the causative Leishmania species in qPCR-positive samples. RESULTS: This study included 245 patients, with 154 (63%) infected in the Pacific region and 91 (37%) infected in the Amazon. Causative Leishmania species were identified in 135 patients (73% of qPCR positives). L. guyanensis was identified in 76% (102/135) of the samples and L. braziliensis in 19% (26/135). The Pacific region had a low prevalence of 6% (5/89) of L. braziliensis. For the first time, we report L. guyanensis from the central Amazon, L. braziliensis from the northern Pacific, and L. lainsoni from both the central Amazon and northern Pacific. Amazon cases had a longer median health-seeking delay in months (2.0, IQR 3.0) than Pacific cases (1.0, IQR 1.5). Prolonged health-seeking delay was associated with older age, Amerindian ethnicity, infection at lower altitudes, non-ulcerative lesions, and lesions on the lower limbs. CONCLUSIONS: In the Pacific region, health-seeking delay is relatively short and L. braziliensis prevalence remains low. Limited access to health care and stigma might explain the prolonged health-seeking delay in the Amazon. We recommend larger studies on the distribution of Leishmania species in Amazon CL cases and additional regional research into diagnostic test accuracy. Furthermore, the determinants of health-seeking delay in Ecuador should be investigated further.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".