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Record W4396658425

Anticoagulant rodenticide toxicity in dogs: A retrospective study of 349 confirmed cases in Saskatchewan.

2024· article· en· W4396658425 on OpenAlexaffabout
Mathieu Victor Paulin, Samantha Bray, Tanarut Laudhittirut, Jeneva Paulin, Barry Blakley, Elisabeth Snead

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsSaskatchewan Hospital
Fundersnot available
KeywordsRodenticideToxicityAnticoagulantMedicineRetrospective cohort studyToxicologyInternal medicineBiology
DOInot available

Abstract

fetched live from OpenAlex

Objective: To evaluate the signalment and clinical, laboratory, treatment, and outcome features of dogs diagnosed with anticoagulant rodenticide (AR) intoxication in Saskatchewan. Animals: We studied 349 dogs. Procedure: Medical records from the Veterinary Medical Centre (Saskatoon, Saskatchewan) between 1999 and 2022 were reviewed. Cases were included if they met at least 1 of the following criteria: owner witnessed the dog ingesting an AR; AR was seen in the vomitus when emesis was induced; the dog had clinical signs of coagulopathy, with elevation of PT ± aPTT that normalized after vitamin K1 therapy, in the presence of appropriate clinical and paraclinical data and the absence of other causes of hypocoagulable state determined by the primary clinician. Results: = 0.017). Eighty-six percent of dogs with AR-induced hemorrhage survived to discharge. Conclusion and clinical relevance: The pleural space was the most common site of hemorrhage. Moderate thrombocytopenia was a common finding. Eighty-six percent of dogs with AR-induced hemorrhage survived to discharge.

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 score0.884
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.026
GPT teacher head0.262
Teacher spread0.236 · 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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