Comparative efficacy of veterinary Ayurveda over conventional allopathic practices on various physiological & hematological parameters in relation to IBD as complex digestive disorder in canine pets of India
Bibliographic record
Abstract
In recent years, pet dogs in India have become integral family members, with increased attention to canine health issues, particularly digestive disorders such as Inflammatory Bowel Disease (IBD). IBD in dogs is characterized by chronic intestinal inflammation, manifesting as vomiting, diarrhoea, abdominal discomfort, and nutrient mal-absorption, with similarities to human IBD and IBS. This study aimed to assess the prevalence, clinical patterns, and therapeutic effects of conventional and Ayurveda treatments on IBD in pet dogs. Conducted over three years (June 2021-May 2024) at the Veterinary Clinical Complex, Kolkata, and selected private clinics in West Bengal, the study included 24 Labrador Retrievers aged 6-12 years. Dogs were assigned to healthy control (Group I), conventional veterinary practice (CVP, Group II), Ayurveda drug-minimum dose (ADMD, Group III), and Ayurveda drug-standard dose (ADSD, Group IV). Physiological parameters (temperature, pulse, respiration), haematological indices (Hb, RBC, WBC, PCV, MCV, platelets, ESR), and faecal and biochemical analyses were monitored. Results showed significant improvement in physiological and haematological parameters in CVP and ADSD groups by day 28, approaching levels of healthy controls. ADMD group exhibited slower recovery. Both allopathic and Ayurveda interventions effectively reduced inflammation, modulated immune responses, and stabilized physiological functions, with herbal therapy showing potential as an adjunct or alternative treatment. This study highlights the clinical relevance of veterinary Ayurveda in managing canine IBD, supporting improved gastrointestinal health, haematological recovery, and overall well-being, and provides evidence for integrating traditional and conventional therapies in veterinary practice.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".