Fetal mummification in the major domestic species: current perspectives on causes and management
Bibliographic record
Abstract
Fetal mummification is an uncommon condition in most domestic species. While most often seen in multiparous and polytocous species like swine, it is also observed in monotocous species when the fetus is retained for a long time. The low prevalence of the condition may help explain the scarcity of information in the literature. To further complicate the study of this phenomenon, the physiological mechanisms that maintain pregnancy vary between species, implying different pathways for the condition. The exact outcome of early fetal mortality is unpredictable, and is influenced by several factors, including the cause of fetal mortality, differences in pregnancy between species, stage of gestation at fetal death, and number of fetuses. Based on our current knowledge of natural fetal mummification events, there are a number of prerequisites for the process of fetal mummification to occur. Examining the circumstances associated with fetal mummification can help scientists better understand the etiology and clinical situation in different species. The objective of this article is to review fetal mummification in the major domestic species: cattle, goats, sheep, horses, swine, dogs, and cats. This paper discusses the clinical situation, the most common and important etiologies, and the treatment approaches for restoring future pregnancy in the female, and where applicable, herd fertility.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".