Hypokaliemia etiological causes in companion animals assessment
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".