Graduate Student Literature Review: Implications of transition cow health for reproductive function and targeted reproductive management
Bibliographic record
Abstract
Negative associations of health disorders with reproductive performance, often measured with pregnancy per AI or the risk of pregnancy loss, have been demonstrated extensively. Most studies investigated common clinical diseases but did not include subclinical disorders comprehensively. They often evaluated cows subjected to hormonal synchronization protocols for timed AI, limiting the ability to understand how disease may affect spontaneous reproductive function, which is essential for targeted management programs with selective hormonal intervention. It is plausible that metabolic and inflammatory disorders have short- and long-term detrimental effects on different features of reproductive function that result in or contribute to reduced fertility. These may include: (1) re-establishment of endocrine function to promote follicular growth and first ovulation postpartum, (2) corpus luteum (CL) function, (3) estrus expression, and (4) uterine environment, fertilization, and embryonic development. In this narrative literature review, we discuss insights and knowledge gaps linking health disorders with these processes of reproductive function. A growing set of observational studies with adequate internal validity suggest that these outcomes may be affected by metabolic and inflammatory disorders that are common in the early postpartum period. A better characterization of these risk factors in multisite studies with greater external validity is warranted to develop decision-support tools to identify subgroups of cows that are more or less likely to be successful in targeted reproductive management programs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".