Factors contributing to the decision to perform a cesarean section in Labrador retrievers
Bibliographic record
Abstract
BACKGROUND: In the past 10 years, the frequency of unplanned cesarean sections in the Labrador Retriever breeding colony at Guiding Eyes for the Blind stayed around 10% (range 5% to 28%). To reduce the number of cesarean sections, factors influencing the occurrence of a cesarean section need to be known. The goal of this study was to identify factors that contribute to the decision to perform a cesarean section. RESULTS: Of the 688 Labrador Retriever litters whelped between 2003 and 2016, 667 litters had sufficient data and remained in the analysis. The target trait was ordinal with the three levels "normal whelping", "assisted whelping" and "cesarean section". A general ordinal logistic regression approach was used to analyze the data. Model selection with possible predictors resulted in a final model including weight of the dam, the weight of the heaviest puppy of a litter, the number of fetuses malpositioned and the quality of uterine contractions. Weight and size of a litter, parity, maternal inbreeding coefficient, whelping season, dam and sire were dropped from the model because they were not significant. The risk of a cesarean section was influenced by the combination of the weight of the dam and the weight of the heaviest puppy in the litter, as well as by the number of malpositioned fetuses and the quality of the contractions. Larger puppies increased the risk of cesarean section especially when the dam had a lighter weight. For dams weighing 23.6 kg and 32.8 kg the predicted probability of a cesarean section was low, with 0.06 and 0.02, respectively, when the heaviest puppy in a litter was light (0.42 kg), contractions were normal and no fetus was malpositioned. However, the probability of a cesarean section was much higher, ranging from 0.24 to 0.08, when the heaviest puppy in a litter was heavy (0.66 kg). CONCLUSIONS: Means to reduce the cesarean section frequency in this Labrador Retriever breeding colony should include genetic selection for ideal puppy weight. In addition, dams with an adult body weight substantially below average should not be selected as breeders in this colony.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".