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Record W4415648560

Emergence of mortality due to centrilobular to massive hepatic necrosis in western Canadian beef calves.

2025· article· en· W4415648560 on OpenAlexaffabout
Devon J. Wilson, Jennifer Davies, Yanyun Huang, Vanessa Cowan, Lindsay Rogers, Barbara Wilhelm

Bibliographic record

VenuePubMed · 2025
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNecrosisDiarrheaDiseaseEpidemiologyAcute tubular necrosisCentrilobular necrosisCause of death
DOInot available

Abstract

fetched live from OpenAlex

Neonatal beef calves can succumb to several common diseases, including diarrhea and pneumonia, but death associated with hepatic disease is uncommon in this age group. Since 2022, a syndrome characterized by massive hepatic necrosis has been observed in western Canadian neonatal beef calves. This case series describes the clinical presentation, pathology, and ancillary testing done in 22 cases, highlighting the young age (1 to 5 d), varied symptoms observed, and unique liver pathology. Based on the pathology, a toxic cause was suspected, and toxic copper concentrations in kidney tissues were detected in 9/22 cases. Further investigation is required to understand this potentially emerging disease and prevent further occurrences. Key clinical message: Practitioners and pathologists should be aware of a new syndrome of hepatic necrosis in neonatal beef calves when evaluating neonatal calf mortality on western Canadian beef cattle operations. Further study is needed to understand the epidemiology of this condition.

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 score0.474
Threshold uncertainty score0.954

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.050
GPT teacher head0.320
Teacher spread0.270 · 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 routes2
Has abstractyes

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