MétaCan
Menu
Back to cohort
Record W3089981949 · doi:10.30954/2277-940x.04.2020.5

Doppler Echocardiographic Reference Parameters in Healthy Labrador Retriever Dogs

2020· article· en· W3089981949 on OpenAlexaboutno aff
Saini Neetu

Bibliographic record

VenueJournal of animal research · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Conditions and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsLabrador RetrieverMedicineDoppler effectVeterinary medicinePathology

Abstract

fetched live from OpenAlex

Thirty-one clinically healthy Labrador retriever dogs of both sexes (18 males and 13 females) were selected for determining Doppler echocardiographic reference values. 2 D and Pulse wave Doppler echocardiography was carried out by using GE Logiq P5 Color Doppler machine. The effect of body weight, age and sex on various doppler echocardiographic parameters were recorded. Twenty four dogs were in body weight range of 20-40 kg and 7 dogs in 40-60 kg range. To study the effect of age on various Doppler echocardiographic measurements, dogs were divided into 4 age groups (1-2, 2-3, 3-5 and >5 years of age). The mitral A wave peak velocity (MA) and ME:MA ratio were significantly (p<0.05) affected by body weight. The pulmonic valve velocity and pulmonic valve pressure were significantly (p<0.05) affected and there was significant negative correlation of pulmonic valve peak velocity (Pulmonary V max) and pulmonary pressure with body weight with r 2 values of 0.160 and 0.120 respectively. Mitral valve (MV) deceleration time was significantly (p<0.05) affected by age. The tricuspid valve deceleration time (TVDecT) was significantly (p0.01) higher in dogs > 5 years of age. Tricuspid A velocity was significantly (p0.01) higher in 2-3 year age group dogs as compared to dogs belonging to age group 3-5 years and > 5 years of age group. The pulmonic valve velocity and pressure were significantly affected by age. The tricuspid valve TE: TA ratio was significantly (p0.05) affected by gender and the ratio was significantly (p0.05) higher in males as compared to females.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.186
GPT teacher head0.412
Teacher spread0.226 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of animal researchSame topicCardiovascular Conditions and TreatmentsFrench-language works237,207