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Record W2409675869 · doi:10.1016/j.jvc.2015.05.001

Management of incidentally detected heart murmurs in dogs and cats

2015· review· en· W2409675869 on OpenAlex
Étienne Côté, Nicole Edwards, Stephen J. Ettinger, Virginia Luis Fuentes, Kristin A. MacDonald, Brian A. Scansen, D. David Sisson, Jonathan A. Abbott

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Veterinary Cardiology · 2015
Typereview
Languageen
FieldMedicine
TopicCardiovascular Conditions and Treatments
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsMedicineHeart murmurCATSAuscultationClinical decision makingInternal medicineCardiologyIntensive care medicine

Abstract

fetched live from OpenAlex

A dog or a cat has an incidentally detected heart murmur if the murmuris an unexpected discovery during a veterinary consultation that was not initially focused on the cardiovascular system. This document presents approaches for managing dogs and cats that have incidentally-detected heart murmurs, with an emphasis on murmur characteristics, signalment profiling, and multifactorial decision-making to choose an optimal course for a given patient.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.861
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.380
Teacher spread0.308 · 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