Clinical manifestations and alterations in urine parameters in canine diabetes mellitus
Bibliographic record
Abstract
The current investigation was carried out in the Jaipur, Rajasthan between April 2022 and September 2022 to assess the clinical symptoms and variations in urine parameters in diabetic canines. Two hundred dogs of varying age, sex and breeds were analysed for the presence of the symptoms such as polydipsia, polyuria, weakness or fatigue, polyphagia, obesity, rapid weight loss and rapidly growing bilateral cataracts. In our study, nine positive diabetic canines revealed, the highest cases of diabetes were found in Labrador breed dogs and age group of more than 6 years of age while females are more prone to diabetes than males. Dogs that were thought to have diabetes mellitus had their blood tested for glucose levels using an in-house glucometer. After 12 hours of fasting, dogs with a blood glucose (random) level above 140 mg/dl were retested, and only those with fasting blood glucose above 140 mg/dl were included in the current investigation. The study also included a healthy control group of 10 canines (dogs). Dogs with diabetes mellitus were tested by having their urine analysed with a standard urinalysis dip-stick kit. Nine dogs were found to have diabetes after initial testing revealed the condition. Ketone bodies, specific gravity, protein and glucose were all shown to be significantly elevated in the urine of diabetic canines, while urine pH was significantly decreased (P<0.01). Canines with diabetes did not have blood or nitrite in their urine. The most common clinical indications of diabetes in dogs were polydipsia, polyuria and weight loss, followed by polyphagia, cataract formation and vomiting.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".