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Record W3014590495 · doi:10.1111/jvim.15745

ACVIM consensus statement guidelines for the classification, diagnosis, and management of cardiomyopathies in cats

2020· article· en· W3014590495 on OpenAlexaff
Virginia Luis Fuentes, Jonathan A. Abbott, Valérie Chetboul, Étienne Côté, Philip R. Fox, Jens Häggström, Mark D. Kittleson, Karsten E. Schober, Joshua A. Stern

Bibliographic record

VenueJournal of Veterinary Internal Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Conditions and Treatments
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsMedicineCardiomyopathyEtiologyHeart failureCATSIntensive care medicineSubclinical infectionLife expectancyCardiologyInternal medicineSudden cardiac death

Abstract

fetched live from OpenAlex

Cardiomyopathies are a heterogeneous group of myocardial disorders of mostly unknown etiology, and they occur commonly in cats. In some cats, they are well-tolerated and are associated with normal life expectancy, but in other cats they can result in congestive heart failure, arterial thromboembolism or sudden death. Cardiomyopathy classification in cats can be challenging, and in this consensus statement we outline a classification system based on cardiac structure and function (phenotype). We also introduce a staging system for cardiomyopathy that includes subdivision of cats with subclinical cardiomyopathy into those at low risk of life-threatening complications and those at higher risk. Based on the available literature, we offer recommendations for the approach to diagnosis and staging of cardiomyopathies, as well as for management at each stage.

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)0.015
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0050.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0050.002
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0080.006

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 head0.229
GPT teacher head0.419
Teacher spread0.190 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations412
Published2020
Admission routes1
Has abstractyes

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