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Hypokaliemia etiological causes in companion animals assessment

2023· article· en· W4365999434 on OpenAlexaboutno aff
L. Yu. Karpenko, A. I. Kozitsyna, А. А. Бахта

Bibliographic record

VenueLegal regulation in veterinary medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicGout, Hyperuricemia, Uric Acid
Canadian institutionsnot available
Fundersnot available
KeywordsHematocritHypokalemiaMedicinePhysiologyCreatinineAnemiaCATSBilirubinAlkaline phosphataseInternal medicineBlood chemistryAnorexiaAlbuminGastroenterologyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Potassium is a vital element involved in ensuring the work of excitable tissues and maintaining the osmotic pressure of all body cells, therefore any of its displacements are critically important both for the diagnosis and prediction of the course of the disease, but also for monitoring treatment. In the presented study, the biochemical blood parameters of small breeds of dogs (Yorkshire Terrier, Toy Poodle, pug, Miniature Pinscher and Pomeranian), large breeds (Labrador Retriever, German Shepherd), cats (Burmese, British, Maine Coon, Russian blue, Scottish Straight and European shorthair) were analyzed. The purpose of the presented study was to identify and assess the frequency of occurrence of etiological causes leading to the development of hypokalemia in cats and dogs of different breeds in the territory of St. Petersburg with subsequent statistical processing of the results obtained. Serum levels of total protein, albumin, globulin, urea, creatinine, bilirubin, glucose, potassium, calcium, phosphorus, as well as the activity of enzymes alanine aminotransferase (AlAt), aspartate aminotransferase (AsAt) and alkaline phosphatase were determined. The parameters of hematocrit, hemoglobin, the number of erythrocytes and leukocytes were determined in the stabilized blood, also according to generally accepted methods. It was found that the most common causes of hypokalemia in cats are chronic kidney disease (41%), dysphagia as a result of dental disease (29%), neoplasms – mainly mammary glands (24%). In dogs of large and small breeds – enteropathies and conditions accompanied by vomiting (32%), blood parasites (16%) and hepatopathy (16%), with a predominance of enteropathies in dogs of both groups and blood parasitic diseases causing anemia in dogs of large breeds. It should be noted that in order to more accurately determine the diagnostic significance and the possibility of determining forecasts, it is necessary to increase the number of samples of animals with an assessment of indicators in dynamics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.083
GPT teacher head0.387
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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