Reproductive emergencies in the bitch: a retrospective study
Bibliographic record
Abstract
This study aimed at analyzing reproductive emergencies (RE) in bitches brought in to a general veterinary hospital during a 24 month-period; evaluating their clinical-epidemiological features; and assessing the results of applied therapy. RE accounted for 11.8% of all female dog emergency clinical cases. Pyometra and dystocia accounted for 56.0% and 32% of the RE, with three (Newfoundland, Siberian Husky, Chow-Chow) and nine breeds (Boston Terrier, French Bulldog, Bernese Mountain Dog, Yorkshire Terrier, St. Bernard, Maltese, Chihuahua, Doberman and Boxer) found at higher risk of pyometra and dystocia, respectively. Fifty-four (96.4%) cases of pyometra were surgically managed, with a mortality of 13.0%. Primary uterine inertia (19 cases) was the main cause (59.4%) of dystocia. Medical treatment was attempted in 23 cases of dystocia (71.9%) but found to be effective in only two of them, leading to a high percentage of caesarian sections (30/32 cases, 93.8%). Both pyometra and dystocia had a high percentage of success following surgery. The use of standardized diagnostic and treatment protocols for the approach of RE allowed favorable outcomes.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".