Surveillance and Prevalence of Canine Reproductive Disorders in Gujarat
Bibliographic record
Abstract
The epidemiological surveillance of canine reproductive disorders was carried out based on total 21852 clinical cases (9159 at the College Clinic, Anand, and 12693 at Polyclinic, Vadodara) attended in dogs over three years. Among them, the overall 486 (2.22 %) and 85 (0.39 %) cases were of gynecological and andrological nature, respectively. Amongst the gynaecological cases, the highest incidence was of pyometra (23.25 %), followed in descending order by mammary tumours (22.22 %), pregnancy diagnosis (16.25 %), elective sterilization (9.88 %), CTVG (7.61 %), proestrus bleeding (5.97 %), pseudo-pregnancy (3.06 %), misalliance (2.67 %), anestrus (2.26 %), dystocia (1.65 %), abortion (1.03 %) and Cesarean (0.82%). Among the andrological cases, the highest cases were of venereal granulomas (31.76 %) followed in descending order by scrotal dermatitis (18.82 %), castration (12.94 %), orchitis (7.06 %), cryptorchidism, paraphimosis and balanoposthitis (5.88 % each), prostatic hyperplasia and testicular tumor (4.70 % each) and testicular hyperplasia (2.35 %). The breed most prone to gynecological disorders was non-descript (51.65 %), Pomeranian (16.25 %), German Shepherd, Labrador (6.79 % each) and Doberman (5.97 %). Maximum cases were in the young age group of 0-5 years (51.02 %), followed by the middle age group of 6-10 years (27.57 %) and older bitches of 11-15 years of age (20.58 %). The major clinical modalities and their management strategies adopted have also been summarized. The results signified the importance of life-threatening diseases like pyometra, mammary tumors, and CTVG in pet dogs in urban areas of middle Gujarat.
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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 | 0.000 | 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) | 0.000 | 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 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".