Epidemiological Aspects of Renal Disorders in Dogs at South-Saurastra Region of Gujarat- A Prospective Study
Bibliographic record
Abstract
Background: Kidney plays a major role in eliminating metabolic wastes from body, in maintaining acid-base balance and production of hormones. Due to their anatomic and physiologic features, kidneys are most susceptible to toxicity and ischemia. No study has been carried out to assess the renal disorders in dog in this region. Hence, the epidemiological study was carried out to record renal disorders in dogs at Junagadh. Methods: The assessment of the incidence of renal disorders in dogs was performed in the hospital cases presented at Veterinary Clinical Complex, COVSAH, KU from September 2022 to August 2023. A total of 2850 caseloads of dogs with different ailments were screened, out of which 170 dogs were diagnosed as renal disorders based on clinical presentation, hematology, serum biochemistry, urine analysis and nephrosonography. Result: The overall incidence of renal disorders in dogs was 5.96%. The higher occurrence of renal disorders was noted in age group of 6-8 years (9.62%) and males (6.61%). The dog breeds that were found higher incidence were Labrador retriever (9.68%) followed by German shepherd (7.24%). Month and Season wise higher incidence was recorded in the month of October (10%) of monsoon (7.61%) season. The highest recorded incidence of renal disorder was nephritis and renal failure (AKI/CKD) (each 35.88%).
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".