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Record W4396558399 · doi:10.1186/s13071-024-06282-w

Canine leishmaniasis in the Americas: etiology, distribution, and clinical and zoonotic importance

2024· article· en· W4396558399 on OpenAlexaboutno aff
Filipe Dantas‐Torres

Bibliographic record

VenueParasites & Vectors · 2024
Typearticle
Languageen
FieldMedicine
TopicResearch on Leishmaniasis Studies
Canadian institutionsnot available
FundersBayer Animal HealthElanco Animal HealthFundação de Amparo à Ciência e Tecnologia do Estado de PernambucoConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsLeishmania infantumLeishmaniaLeishmaniasisBiologyCanine leishmaniasisParasitologyEtiologyWildlifeTransmission (telecommunications)Leishmania braziliensisVeterinary medicineZoonosisDisease reservoirZoologyVirologyImmunologyVisceral leishmaniasisEcologyCutaneous leishmaniasisPathologyMedicineParasite hosting

Abstract

fetched live from OpenAlex

Canine leishmaniasis is a widespread disease on the American continent, with cases reported from Uruguay to the USA and Canada. While numerous Leishmania spp. have been reported in dogs in this region, Leishmania infantum and Leishmania braziliensis are the most common etiological agents of canine leishmaniasis from a continental perspective. Nonetheless, other species may predominate locally in some countries. The participation of dogs in the transmission cycle of various Leishmania spp. has long been speculated, but evidence indicates that their role as reservoirs of species other than L. infantum is negligible. Various native wildlife (e.g., small rodents, marsupials, sloths, and monkeys) are, in fact, the primary hosts of Leishmania spp. in the Americas. In this review, an updated list of Leishmania spp. infecting dogs in the Americas is presented, along with their distribution and clinical and zoonotic importance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.036
GPT teacher head0.375
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations46
Published2024
Admission routes1
Has abstractyes

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