Prevalence of canine renal insufficiency: A decade-long retrospective study in and around Patna, Bihar
Bibliographic record
Abstract
This retrospective study investigates the prevalence and epidemiological factors associated with renal insufficiency in dogs within Patna, Bihar, India. Out of 5,700 dogs with various illnesses, 317 were diagnosed with renal insufficiency, identified by serum creatinine levels exceeding 5 mg/dL. The study assessed the incidence of renal insufficiency across different demographics, including age, sex, breed, and season. The highest incidence was observed in dogs aged 8-10 years, with males showing a higher prevalence than females. Pomeranians (32.18%), Labrador Retrievers (25.55%), and German Shepherds (20.82%) were the most affected breeds. Seasonally, the post-monsoon period (September-November) exhibited the highest incidence (37.85%) of renal disorders. Laboratory investigations revealed significant haematological and biochemical alterations in affected dogs, including lower haemoglobin, total erythrocyte count, and packed cell volume, alongside elevated serum levels of blood urea nitrogen (BUN), creatinine, potassium, and phosphorus. Ultrasonography indicated decreased renal size and increased echogenicity in dogs with renal insufficiency. The study concludes that renal insufficiency in dogs is most prevalent in older males and certain breeds, particularly during the post-monsoon season. Regular monitoring of serum BUN and creatinine levels is recommended for early diagnosis and management of this condition.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".