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Record W4387012984 · doi:10.2460/ajvr.23.07.0172

Hyperthyroid cats have altered pulmonary arterial hemodynamics but rarely have intermediate or high probability of pulmonary hypertension

2023· article· en· W4387012984 on OpenAlexaff
Caroline Billings, Carol R. Reinero, Isabelle Masseau, Jennifer Bryant, Kelly Wiggen

Bibliographic record

VenueAmerican Journal of Veterinary Research · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Conditions and Treatments
Canadian institutionsUniversité de Montréal
FundersUniversity of Missouri
KeywordsCATSMedicineEuthyroidCardiologyPulmonary hypertensionInternal medicineHemodynamicsPulmonary arteryThyroid

Abstract

fetched live from OpenAlex

OBJECTIVE: Apply the 3-site echocardiographic metrics utilized to assess pulmonary hypertension (PH) probability in dogs and humans to feline echocardiographic examinations to investigate the translatability of this scheme and subsequent enhancement of detection of PH in cats. ANIMALS: 27 client-owned cats (euthyroid [n = 11] and hyperthyroid [16]). METHODS: This was a single-center, prospective, observational case-control study. Demographic, physical examination, and echocardiographic data from hyperthyroid and euthyroid cats were compared via Fisher exact test and Kruskal-Wallis test. RESULTS: Hyperthyroid versus euthyroid cats had significantly greater right atrial area index values and were more likely to have late-peaking main pulmonary artery pulsed-wave flow profiles. Two hyperthyroid cats had measurable tricuspid regurgitation tracings (one with a high probability of PH and another with a low probability of PH). CLINICAL RELEVANCE: Hyperthyroid cats demonstrated altered pulmonary arterial hemodynamics and lacked consistent intermediate or high probability of PH. The 3-site echocardiographic metrics scheme is applicable for the evaluation of right-sided cardiac and pulmonary arterial hemodynamics in cats. Further research is needed to determine reference ranges in larger populations of healthy cats and those with high clinical suspicion for PH.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.121
GPT teacher head0.370
Teacher spread0.249 · 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 teacher head, 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

Citations4
Published2023
Admission routes1
Has abstractyes

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