Assessment of the etiological causes of hyperkalemia in dogs
Bibliographic record
Abstract
Hyperkalemia is a marker of many disorders in different species of animals and birds, leading to a significant number of pathophysiological abnormalities. However, dogs have no literature data on the features of potassium metabolism and etiological causes of hyperkalemia in different breeds, especially when taking into account significant differences in size and conditions of keeping. The purpose of the presented study was to identify and assess the frequency of occurrence of etiological causes leading to the development of hyperkalemia in dogs of different breeds in the territory of the city. St. Petersburg with subsequent statistical processing of the obtained results.In the presented study, the analysis of biochemical blood parameters of dogs of small breeds (Yorkshire Terrier, Toy Terrier, Pomeranian, dachshund, tsvergpincher, Chihuahua, Shih Tzu), large breeds (Labrador Retriever, German Shepherd, Dalmatian, American Staffordshire Terrier, Central Asian Shepherd, Husky) enrolled in private veterinary clinic of St. Petersburg in the summer-autumn period.It was found that the most common causes of hyperkalemia in small breed dogs include chronic kidney disease (25%), oncological diseases (18%), infectious processes (pyometra) (14%), heart disease (14%). In dogs of large breeds, the main role in the development of hyperkalemia is played by diseases of the urinary system – 24% (of which chronic kidney disease - 14%, acute renal insufficiency – 5%, bacterial cystitis - 5%), endocrinopathy - 19% (of which diabetes mellitus – 14%, hypothyroidism - 5%), oncological diseases (19%), orthopedic disorders (19%), neurological disorders (14%).
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".