Déterminisme génétique du pyomètre : synthèse bibliographique et établissement d'une cohorte multi-races
Bibliographic record
Abstract
Pyometra is a disease with numerous risk factors, whether anatomical, epidemiological, linked to hormonal treatment or racial predisposition. A study published in 2021 by Arendt et al. highlighted a possible genetic predisposition in Golden Retrievers. Pyometra is probably a polygenic disease. According to Arendt et al., there is a statistical association between pyometra and the ABCC4 gene, but other genetic factors are probably also involved. The ABCC4 gene codes for a protein called MRP4, whose established role is that of a multitransporter of molecules, notably inflammation molecules and prostaglandins. Our aim was to study the existence of a racial predisposition to pyometra in a population of bitches seen at the CHUV-AC of EnvA. To this end, a retrospective study of all cases of pyometra presented to the ENVA's CHUV-AC between 2002 and 2022 was carried out. The retrospective study showed that certain breeds were over-represented among the bitches presented to ENVA's CHUV-AC for pyometra. These include the Rottweiler, Yorkshire terrier, Labrador, Poodle, Golden retriever, German shepherd, American Staffordshire terrier, Westie, Cavalier King Charles, as well as the Bichon, Bull Terrier and English bulldog. However, these results may be skewed by the over-representation of certain breeds in the Paris region. One of the aims of this thesis was to establish a multi-breed cohort of bitches with pyometra in order to create a DNA bank for future comparative genetic studies. A multi-breed cohort of 19 bitches with pyometra was set up, and their DNA was extracted and stored in the national biobank cani-DNA.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".