Influence of Age Breed and Sex on Incidence of Renal Disorders in Dogs
Bibliographic record
Abstract
Background: Kidneys play an essential role in health, disease, and growth. Renal disorders are among the most common ailments of dogs and contribute substantially to canine mortality, particularly in older dogs. Fewer published reports are documenting the prevalence of renal diseases in dogs in India. The current study was undertaken to find out the incidence of renal disorder in dogs based on their age and breed and sex. Methods: The assessment of the incidence of renal disorders in dogs was done in the clinical cases reported at Referral Veterinary Polyclinic, IVRI during the period i.e. February 2010 to January 2011. The total numbers of 880 cases of dogs suffering from different ailments were reported during this period, out of which 63 dogs were suspected and screened for renal disorders based on clinical signs, ultrasonographic findings, serum and urinary biochemical alterations and urine analysis. Result: The overall incidence of renal disorders was 7.15% recorded according to the age of dogs. No renal disorders were detected in the dogs less than 6 years of age. 3.26% dogs of 6-8 year age group were confirmed for kidney diseases. Whereas 9.30% and 13.94% dogs in the age groups of 8 -10 year and ≥ 10 years, respectively had renal disorders. The breed wise renal disorders in dogs showed the highest incidence in Labrador dogs followed by Bulldogs, Dalmatian, Great Dane and Rottweiler, Doberman, German Shepherd and Pomeranian. Interestingly lowest incidence was recorded in the mixed or non-descript breed. Out of 63 dogs, 36 male (57%) and 27 female (43%) dogs were confirmed for renal disorders indicating a higher prevalence of renal diseases in males than females.
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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".