Total and partial ovariohysterectomy in seven mares
Bibliographic record
Abstract
REASONS FOR PERFORMING STUDY: Ovariohysterectomy appears to have a low mortality rate in mares, but the procedure needs to be reviewed because of the high risk of life-threatening complications. HYPOTHESIS: That ovariohysterectomy can be effective treatment for a variety of uterine diseases in mares and carries a good prognosis. METHODS: Diagnosis, clinical data, surgical technique, post operative care, complications and outcome were recorded from medical records of 7 mares that underwent total (6) and partial (1) ovariohysterectomy at the University of Illinois from 1994 to 2001. RESULTS: The indications for ovariohysterectomy were chronic pyometra (4 mares), chronic uterine torsion (n = 2) and chronic intramural haematoma (n = 1). Surgical exposure was difficult but was improved by traction on stay sutures and right-angled clamps. In some cases, application of the TA-90 autosuture instrument as a right-angled clamp to the caudal part of the uterus improved access to the uterine stump. The most common post operative complications were decreased faecal output, decreased intestinal sounds (4 mares) and mild abdominal pain (2). Two mares had mild to moderate incisional infections. Other previously reported complications, such as haemorrhage, septic peritonitis, uterine stump infection or necrosis, and diarrhoea, did not occur. All mares survived over follow-up periods of 6 months to 5 years and were used for riding (6 mares) and embryo transfer (1 mare, after partial ovariohysterectomy). CONCLUSIONS AND POTENTIAL RELEVANCE: According to this study, the prognosis for mares after ovariohysterectomy appears to be good, despite the technical difficulties of the procedure. The prevalence of life-threatening complications can be lower than reported.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".