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Record W4399317351

A retrospective study of the prevalence in equine postmortems of cranial mesenteric arteritis caused by <i>Strongylus vulgaris</i> in Alberta (2010 to 2022).

2024· article· en· W4399317351 on OpenAlexaffabout
Katrice A Domshy, Ashley E. Whitehead, Jocelyn Poissant, Dayna Goldsmith, Carolyn Legge, Cameron G. Knight, Erin Zachar, Sara Skotarek Loch, Jennifer Davies

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldMedicine
TopicAbdominal vascular conditions and treatments
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineArteritisRetrospective cohort studyPathology
DOInot available

Abstract

fetched live from OpenAlex

Background: in the Canadian horse population. Objective: cranial mesenteric arteritis in horses submitted for postmortem examination to the Diagnostic Services Unit (DSU) at the University of Calgary Faculty of Veterinary Medicine. Animals and procedure: We conducted a retrospective review of all equine postmortem cases submitted to the DSU between July 1, 2010 and June 30, 2022. Over 12 y, 510 horses > 2 mo of age from Alberta were submitted to the DSU for necropsy. Active cases were defined as those with endarteritis and thrombosis in the CMA or its branches. Those cases with only intimal scarring of the CMA were classified as historical. Results: cranial mesenteric arteritis was documented in 6.1% (31/510) of equine postmortems and the sequelae of verminous arteritis were the cause of euthanasia or death in 1.5% (8/510) of the cases submitted. Conclusion and clinical relevance: should be taken into careful consideration when implementing antiparasitic control strategies. Practitioners should remain current on prevention, diagnosis, and treatment of this potentially reemerging and fatal equine disease.

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 score0.659
Threshold uncertainty score0.677

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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