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Record W2984837452 · doi:10.30954/2277-940x.05.2019.12

A Retrospective Analysis of Dilated Cardiomyopathy in Labrador Retrievers

2019· article· en· W2984837452 on OpenAlexaboutno aff
K. Jeyaraja

Bibliographic record

VenueJournal of animal research · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Conditions and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDilated cardiomyopathyMedicineCardiomyopathyInternal medicineCardiologyHeart failure

Abstract

fetched live from OpenAlex

The present study was conducted to record the incidence, clinical presentation, electrocardiographic, radiographic, laboratory, two dimensional echocardiography, M-mode echocardiography, pulsed wave Doppler and color flow Doppler findings in Labrador Retrievers with Dilated Cardiomyopathy (DCM) for a period of five years from 2013 to 2018. It included 210 healthy dogs and 327 confirmed cases of DCM. The incidence of dilated cardiomyopathy in Labrador Retrievers was found to be 7.49 per cent in the present study. On radiography, cardiomegaly and pulmonary edema were the major findings observed. In echocardiography, increased left ventricular end diastolic dimension and systolic dimension, reduced fraction shortening, increased E-point sepal separation, increased Left atrium (LA)/Aorta (AO) ratio, decreased ejection fraction, increased end diastolic volume and end systolic volume were noticed. On pulsed wave Doppler echocardiography reduced pulmonary artery (PA), Aorta (AO), left ventricular outflow tract (LVOT), right ventricular outflow tract (RVOT) velocities were recorded. Mild to moderate regurgitation was observed in Mitral and Tricuspid valve by color flow Doppler echocardiography. M-mode derived chamber dimensions, E-point septal separation, ejection fraction, fractional shortening were reliable parameters in diagnosing Dilated Cardiomyopathy in Labrador Retrievers. Pulsed wave Doppler and color flow Doppler were useful in assessing velocity and flow pattern across valves.

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)0.002
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 score0.018
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
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 head0.035
GPT teacher head0.370
Teacher spread0.334 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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