Accuracy of prediction of canine litter size and gestational age with ultrasound
Bibliographic record
Abstract
Different sonographic criteria have been developed to estimate canine fetal age, including fetal mensuration and assessment of fetal organ development. This retrospective study assessed the accuracy of gestational age and litter size predictions in 76 bitches using one of two techniques. The first method used the differential features of fetal organ development that occur in early and mid pregnancy, based on published tables for beagles. The second method used biparietal head and trunk diameters to predict gestational age based on tables published for late gestational Labrador Retrievers. The accuracy of the two methods was compared and the effect of maternal body weight and litter size evaluated. Litter size and maternal body weight did not affect the accuracy of gestational age prediction. Using a combination of both methods, the overall accuracy of predicting parturition date within 65 +/- 1 day and +/- 2 days was 70.8% and 86.1%, respectively. The correct litter size was predicted in 65% of cases, and in 89.5% of cases for +/- 1 pup. Pearson's correlation between actual litter size and predicted litter size was high (R = 0.957, P < 0.001). The organ development method of predicting gestational age was more accurate than late gestational fetal mensuration (P = 0.019). The optimum time for sonographic estimation of fetal age and litter size is early and mid pregnancy.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".