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Record W4416104786 · doi:10.1590/1678-5150-pvb-7660

An outbreak of Potomac horse fever

2025· article· pt· W4416104786 on OpenAlexaff
Fabrício Moreira Cerri, Roberta Martíns Basso, Natália Botega Pedroso, Wanderson Adriano Biscola Pereira, José P. Oliveira‐Filho, Rogério Martins Amorim, J. D. Baird, Luis G. Arroyo, Alexandre Secorun Borges

Bibliographic record

VenuePesquisa Veterinária Brasileira · 2025
Typearticle
Languagept
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsOutbreakFecesDiarrheaHorseInfectious disease (medical specialty)Clinical disease

Abstract

fetched live from OpenAlex

ABSTRACT: Potomac horse fever (PHF) is caused by Neorickettsia risticii and the recently identified Neorickettsia findlayensis and is characterized by clinical signs such as diarrhea, hyporexia, and lethargy. PHF outbreaks are uncommon. The aim of this report is to describe the epidemiological, clinical, clinicopathological, and molecular aspects of a PHF outbreak in Brazil. An outbreak of gastrointestinal disease was investigated, and clinical and laboratory evaluations were performed in affected animals. A total of 37 out of 216 horses (17%) were affected by diarrhea, lethargy, and hyporexia during the outbreak. Only one horse developed laminitis, and five horses died. Blood and fecal samples from 17 of 37 affected mares were tested for N. risticii by qPCR. N. risticii was detected in 16/17 fecal samples and 11/16 blood samples. Oxytetracycline (BID) was an effective treatment for affected horses. The genetic analysis of N. risticii revealed similarity with strains described in North America. PHF must be included in the differential diagnosis of adult horses presenting diarrhea in Brazil.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.017
GPT teacher head0.299
Teacher spread0.282 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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