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Record W2782467202

The association of increased serum myocardial enzymes concentration with aberrations in the electrocardiograms of dogs

2017· article· en· W2782467202 on OpenAlexaboutno aff
Sipra Panda, Riddhi Pandey, Swagat Mohapatra, Pravas Ranjan Sahoo, Tushar Jyotiranjan, Akshaya Kumar Kundu

Bibliographic record

VenueJournal of Entomology and Zoology Studies · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Conditions and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLabrador RetrieverLactate dehydrogenaseCreatine kinaseInternal medicineCardiologyMyocardial infarctionCreatineEdemaElectrocardiographyPathologyEnzymeBiology
DOInot available

Abstract

fetched live from OpenAlex

The study was undertaken to assess the concordance of increased serum myocardial enzymes and myocardial injury with certain aberrations in electrocardiograms of dogs. Labrador Retriever dogs aged between 5 to 10 years and showing symptoms of cardiac problems like nocturnal coughing, exercise intolerance, cyanotic mucus membrane, inappetance, edema etc were considered for the study and apparently healthy Labrador Retriever dogs aged between 5 to 10 years having no history of cardiac disorders were enzymatically screened to get control values. Electrocardiographic alterations like tall T waves were associated with increased serum levels of CKMB (Creatine Kinase MB isoenzyme), LDH (Lactate Dehydrogenase) and AST (Aspartate Aminotransferase) while ECG aberrations like ST segment abnormalities and arrhythmia were associated with increased levels of CKMB and LDH in the serum. Our study concluded that myocardial infarction might be the reason behind certain ECG findings which are often believed to be of non-ischemic origins.

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 score0.007
Threshold uncertainty score0.180

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.298
Teacher spread0.283 · 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
Published2017
Admission routes1
Has abstractyes

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