Short communication: Prepartum plasma insulin-like growth factor-I concentrations based on day of insemination are lower in cows developing postpartum diseases
Bibliographic record
Abstract
Because peripartal production diseases are prevalent in dairy cows, early recognition is crucial. Several studies reported metabolic variables as risk predictors for subsequent diseases. To improve on-farm testing and application of those methods, the sampling procedure should take into account variation in gestation length. Furthermore, additional variables indicating cows at risk of any production disease should be sought. Therefore, the objective was to characterize differences between cows with and without postpartum production disease (retained fetal membranes, ketosis, hypocalcemia, abomasal displacement, metritis, mastitis) by prepartum measurement of serum nonesterified fatty acid (NEFA) and plasma insulin-like growth factor (IGF)-I concentrations relative to the artificial insemination (AI) that established pregnancy. Blood was collected from 41 Holstein Friesian cows on 235 to 241, 242 to 248, 249 to 255, 256 to 262, 263 to 269, 270 to 276, 277 to 283, and 284 to 290 d after AI. Health status was assessed daily for 3 wk after calving; 25 cows (66%) had at least one production disease. Cows developing postpartum diseases had higher mean serum NEFA concentrations (450 ± 26 μmol/L; mean ± SE) and lower plasma IGF-I concentrations (78 ± 6 ng/mL) prepartum compared with healthy cows (259 ± 19 μmol/L and 117 ± 8 ng/mL, respectively). In conclusion, because of substantial variation among cows in gestation length, blood samples should be collected and studies performed on risk prediction relative to AI rather than expected date of calving. As the somatotropic axis is one of the key regulators of metabolic adaption for onset of lactation, IGF-I might be a useful variable to differentiate between cows susceptible to production diseases and cows that are able to adapt adequately within the transition period and remain healthy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".