Association of age, breed, estrus and mating history in occurrence of pyometra
Bibliographic record
Abstract
The study was aimed to analyze the influence of age, breed, estrus cycle and parity in the occurrence of canine pyometra and to assess the treatment of choice being followed for pyometra. For this study, case records of pyometra in dogs, presented between January 2013 to December 2017 were scrutinized and the influence of different factors on the incidence of pyometra was worked out. A total of 108 cases with the history of pyometra were presented in TVCC in period of 5 years. The pyometra observed in the middle to old age (>6 years) predominately (45.37%, mean age- 5.65 ± 0.3 years). The incidence was more in non-descript breeds (29.63%) followed by Labrador (24.07%) and Pug (15.74%). The disease was more prevalent in nulliparous dogs (81.82%) as compared to primiparous and pluriparous dogs which accounted to be 18.18%. There was history of estrus 16-60 days prior to the occurrence of disease in 74.63% dogs, of them, 17.05% (15/88) had mating history. Ovariohysterectomy (37.03%) was principle approach opted for treatment of pyometra. Other treatment opted for treatment of pyometra were antibiotics (24.07%), methergine (20.37%), PGF2α analogue (16.67%) and mifepristone (0.93%).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".