Prevalence of Major Reproductive Disorders in Canines with Reference to Age, Sex and Breed in Central Gujarat
Bibliographic record
Abstract
A retrospective study was undertaken to assess the prevalence of various reproductive disorders attended over the last three years (2017-20) at VCC, Anand in either sex of canine. Screening of 9963 case records revealed 602 (6.04 %) cases of gynecological disorders and 23 (0.23 %) cases of andrological disorders. The highest cases in female dogs were of pregnancy diagnosis (30.89 %), followed in descending order by pyometra (25.41 %), mammary tumor (11.46 %), CTVG (7.81 %), spaying (5.65 %), pseudo-pregnancy (4.32 %), dystocia (3.32 %), vaginal prolapse (2.99 %), misalliance (2.32 %), abortion (0.99 %), normal whelping (0.49 %) and miscellaneous (3.82 %). Among the male dogs, the highest cases were of venereal granuloma (21.74 %), followed by phimosis (17.39 %), balanoposthitis (13.04 %), prostate enlargement (13.04 %), scrotal edema (13.04 %), castration (8.69 %), testicular hyperplasia (4.35 %), testicular tumor (4.35 %) and orchitis (4.35 %). The breeds most prone to gynecological disorders were Labrador (29.57 %), followed by Mongrel (22.42 %), Pomeranian (11.79 %), German Shepherd (11.46 %), Pug (6.64 %), Rottweiler (6.15 %), Doberman (3.50 %), Golden Retriever (3.17 %), and others. Similarly, the highest occurrence of gynecological cases was found in bitches of 0-3 years age group (46.84 %), followed by 4-6 years (23.75 %), 7-9 years (14.29 %) and 10-12 years of age (11.30 %). The higher gynecological cases recorded in Labrador and Mongrel breeds and in younger age groups of dogs could be due to the higher gross population of these animals in the study area of central Gujarat.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".