Multicenter study of factors associated with nonsurvival in hospitalized periparturient goats
Bibliographic record
Abstract
BACKGROUND: Periparturient reproductive complications appear to be common in hospitalized goats. More information is needed about periparturient reproductive complications and survival in goats with these conditions. OBJECTIVE: Identify exposure factors associated with nonsurvival in periparturient does hospitalized ≤1 day or ≥2 days. ANIMALS: A total of 198 periparturient does presented to 9 university veterinary hospitals from October 2021 to June 2022. METHODS: Multicenter, matched case-control study. Conditional logistic regression was used to identify exposure factors associated with nonsurvival in periparturient does hospitalized ≤1 day or ≥2 days. RESULTS: Overall doe survival was 79% (156/198). Survival in the 1st day of hospitalization was 71% (52/73) and survival in does hospitalized ≥2 days was 83% (104/125). Among goats hospitalized ≤1 day, labor duration before admission (odds ratio [OR] = 4.8; P = .04), uterine tears (OR = 48.2; P < .001), and vaginal/perineal trauma diagnosed during hospitalization (OR = 6.2; P = .03) were associated with nonsurvival. Among goats hospitalized ≥2 days, factors associated with nonsurvival included labor duration before admission (OR = 6.2; P = .004), pregnancy toxemia (OR = 6.07; P = .04), and Cesarean section (OR = 11.35; P = .02). CONCLUSIONS AND CLINICAL IMPORTANCE: Longer labor duration before admission is an important predictor of nonsurvival in hospitalized does. Clients should be educated that early detection and veterinary care are critical for improving outcome in periparturient does.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".