Twice upon a time: The progression of canine visceral leishmaniasis in an Argentinean city
Bibliographic record
Abstract
Canine Visceral Leishmaniasis (CVL) prevalence, spatial distribution and associated factors were assessed in four locations in Iguazú department in 2014 and in Puerto Iguazú city again in 2018. The city areas were divided into a grid of 400x400m cells. All cells were sampled in 2014 and a random subsampling was developed in 2018. In each cell, five dogs clustered in a 'critical scenario' (prone to have vectors) were sampled. A rapid immunochromatographic dipstick was used to detect antibodies against Leishmania infantum, confirming by lymph node smears observation and PCR. For Puerto Iguazú, Generalized Linear Models (GLMs) were constructed considering environmental, dog and clinical variables. Pearson's Chi square and Fisher's exact tests were employed to evaluate the association between CVL, dog clinical signs and infestation with other parasites. Cartographic outputs were made and Moran's I indices were calculated as spatial autocorrelation indicators. CVL prevalence rates were 26.18% in 2014 and 17.50% in 2018. No associations were established in environmental models, but dog age and repellent use were significant when running 2014 dog models. Clinical models showed significant associations between seropositive dogs and ophthalmological, dermal signs and onychogryphosis in 2014. In 2018, only adenomegaly was associated. The results of global Moran´s I were not significant but regarding local analysis, six sites in 2014 and one in 2018 presented autocorrelation with neighboring sites. The decrease in CVL prevalence may be associated to transmission stabilization, which could explain the lack of associations with dog-related variables. Further, spatial distribution of CVL is a poor evidence for design of transmission control measures but could be important in case of intensive parasite circulation or when the first autochthonous cases appear. For control success, sensitivity of diagnostic methods, political will and adequate material resources remain critical. Modeling of multiple variables will be required to identify factors that drive disease stabilization/destabilization.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".