Bibliographic record
Abstract
PRACTICAL RELEVANCE: Pyometra is a commonly occurring uterine disease in cats that often leads to loss of breeding potential and, in some cases, can be life threatening. An increased incidence of cystic endometrial hyperplasia (CEH) and pyometra is seen with age. Most queens present with uterine lesions after 5-7 years of age (average 7.6 years, range 1-20 years). Clinical signs most commonly occur within 4 weeks of the onset of oestrus in queens that are either mated, spontaneously ovulate or are induced to ovulate (mechanical stimulation or hormone induction). The disease is most often observed in dioestrus. CLINICAL CHALLENGES: Queens with pyometra often go undiagnosed as there may be few or only very mild clinical signs and laboratory changes. For example, the classic sign of mucopurulent bloody vulvar discharge often goes unnoticed. Abdominal ultrasound is the best tool for diagnosis of pyometra and for monitoring response to therapy. PATIENT GROUP: Classically, middle-aged/older nulliparous intact queens present with pyometra. However, so-called 'stump pyometra' can occur if ovarian tissue is left behind during ovariectomy or ovariohysterectomy (ovarian remnant syndrome). Queens treated with exogenous steroid hormones such as high doses of megestrol acetate or medroxyprogesterone acetate for oestrus prevention can also develop CEH and pyometra. EVIDENCE BASE: There has been little published to date on CEH, endometritis and pyometra in the queen and most of the currently available information has been extrapolated from studies carried out in the bitch. The queen and the bitch have very different reproductive physiology; thus, further research and investigation into the precise aetiopathogenesis of these disease processes of the uterus in the queen is warranted. AUDIENCE: This review is aimed at clinicians working in small animal practice, especially those in countries where surgical sterilisation is not practised as commonly as in the United States, Canada or Australasia, and who will therefore see a greater proportion of intact queens.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".