Advantages and difficulties of ultrasound analysis to determine the fertile period in bitches
Bibliographic record
Abstract
Definition the fertile period is one of the most important factors contributing to fertilization. Therefore, this analysis is important to solve the problem of infertility. The methods that available today do not provide the expected result, because the vast majority of unsuccessful mating result are related to the incorrect determination of the ovulation period. Therefore, it is important to assess the condition of the ovaries, and the prospect is ultrasound examination, which is an effective method of monitoring the reproductive organs. Ultrasound analysis is a non-invasive and effective method to establish the fertile period in bitches which makes it possible to track changes in the ovaries, detect follicles, their number, and determine the true (morphological) signs of ovulation. Clinical-visual and hormonal studies which are based on the indirect determination of the optimal period of female insemination have the advantages in compare with vaginal cytology. Particularly, the use of ultrasound in real time allows to visualize the morphological changes of the ovaries and uterus – the irregular shape of the ovaries with large (0.6-1.2 cm) anechoic structures of a rounded or oval shape with a thin capsule, thickening of the endometrium (up to 0.5-0.8 cm) with a hypoechoic structure of the uterine horns. However, in our research we encountered certain difficulties, which turned out to be the same as other authors. Echography of the ovaries can seem a bit complicated, because the follicles that persist in them have a picture similar to that before and after ovulation. Some ovulated follicles do not always completely collapse (fall) during ovulation and are gradually replaced by luteal tissue while maintaining an echogenic picture on the days of the next ovulation. Non-ovulated follicles can complicate sonography interpretation. To diagnose ovulation, an ultrasound analysis should be performed twice a day, which is inconvenient for the owners. But if the ultrasound test is carried out less often than aforementioned number, there is a risk to miss the moment of ovulation, because the follicles before and after ovulation are very similar. An even more significant problem is the location of the ovaries in bitches – costovertebral angle surrounded by fat and an ovarian sac, which can create technical difficulties. In addition, this diagnostic approach may be more difficult in fat or very large dogs, as well as in dogs with thick skin (Shar Pei, Newfoundland).
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".