MétaCan
Menu
Back to cohort
Record W4307293201 · doi:10.2460/ajvr.22.05.0085

Canine mitral valve size as measured by computed tomography

2022· article· en· W4307293201 on OpenAlexaboutno aff
Wanda J. Gordon‐Evans, K. Helena Montin, Christopher P. Ober, Jessi L. Coryell, Andrea Castilla

Bibliographic record

VenueAmerican Journal of Veterinary Research · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Conditions and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCircumferenceMedicineMitral valveIntraclass correlationComputed tomographyTrigone of urinary bladderNuclear medicineAnatomyAnterior commissureRadiologyCardiologyMathematics

Abstract

fetched live from OpenAlex

OBJECTIVE: To measure the mitral annulus in dogs. Our hypothesis was that mitral measurement would be possible and consistent among observers using CT. SAMPLE: Thoracic CT scans of dogs without known heart disease. PROCEDURES: Five trained investigators measured 4 aspects of the mitral valve and the fourth thoracic vertebrae (T4) length using multiplanar reformatting tools. Ten randomly chosen animals were measured by all investigators to determine interobserver reliability. RESULTS: There were 233 CT scans eligible for inclusion. Dogs weighed 2 to 96 kg (mean, 28.1 kg), with a variety of breeds represented. Golden Retrievers (n = 28) and Labrador Retrievers (n = 37) were overrepresented. The intraclass correlations were all greater than 0.9, showing excellent agreement between observers. The means and SDs of each measurement were as follows: trigone-to-trigone distance, 17.2 ± 4.7 mm; the remaining circumference, 79.0 ± 17.5 mm; commissure-to-commissure distance, 30.8 ± 6.5 mm; septal leaflet-to-lateral leaflet distance, 26.3 ± 6.0 mm; T4 length, 16.9 ± 3.1 mm; and the total circumference normalized by T4, 5.7 ± 0.7 mm. CLINICAL RELEVANCE: This study provides information that may help in the development of future treatment for mitral valve dysfunction and subsequent annular enlargement.

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 score0.257
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.055
GPT teacher head0.372
Teacher spread0.317 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Veterinary ResearchSame topicCardiovascular Conditions and TreatmentsFrench-language works237,207