The use of Sonography for the prediction of pregnancy status in a few canine cases
Bibliographic record
Abstract
Records of twelve dogs presented either for pregnancy confirmation or other obstetrical complaints at the Veterinary Teaching Hospital, University of Agriculture, Abeokuta, Ogun State between July, 2008 and June, 2010 were reviewed. Abdominal ultrasounds were performed in all the dogs in right lateral recumbency using a portable ultrasound machine (Kaixin KX 2000R, Xuzhou, China). In this study, the mean age of the dogs was 2.6 ± 0.85 years (age range = 1.5-4 years).The dogs comprised of Alsatian (6), Alsatian crosses (2), Rottweiler (2) Labrador cross (2), Boerboel (3) and local dogs (2). Gestational sac was observed in 2 (16.7%) dogs, foetal sac in 6 (50%) dogs, foetal skeletal structure in 2 (16.7%) dogs and foetal heart beat in 4 (33.3%) dogs. In four dogs, no sonographic changes were observed and were thus diagnosed as non-pregnant. The mean gestational length (based on the predicted gestational age and the time difference between presentation and parturition) of the dogs was 8.5 ± 0.44 weeks (range = 8-9 weeks). All the dogs that were predicted pregnant eventually delivered with a prediction accuracy of hundred percent. It was therefore concluded that detection of foetal and extra-foetal structures by ultrasonographic examination provides a safe and reliable method of confirming canine pregnancy and predicting gestational age. Trop. Vet . Vol. 29 (4) 1-9 (2011)
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".