Association between metabolic diseases and the culling risk of high-yielding dairy cows in a transition management facility using survival and decision tree analysis
Bibliographic record
Abstract
The objective of this study was to assess the association between individual metabolic diseases (MD) and multiple MD (MD+) in the transition period (±3 wk relative to calving) and the culling risk in the first 120 d in milk (DIM) in Holstein-Friesian dairy cows. Health records from a transition management facility in Germany with 1,946 calvings were analyzed in a 1-yr cohort via survival analysis and a decision tree model. The recorded MD were milk fever (MF), retained placenta (RP), metritis (METR), ketosis (KET), displaced abomasum (DA), twinning (TWIN), and clinical mastitis (MAST). The overall culling within 120 DIM was 18%. The 120 DIM culling risk for healthy cows (64.8% of the total) was 13%, whereas it was 25% for MD (24.5%) and 33% for MD+ (10.7%) cows. The 120 DIM culling risk (%) for each MD and MD+, respectively, was 34.6 and 48 for MF and MF+, 15 and 31.5 for RP and RP+, 9.4 and 22.2 for METR and METR+, 30.7 and 37.3 for KET and KET+, 56.1 and 46.8 for DA and DA+, 30.3 and 34 for TWIN and TWIN+, and 36.6 and 27.8 for MAST and MAST+. Moreover, the incidence risk (%) for each MD and MD+, respectively, was 4 and 2.6 for MF and MF+, 1 and 2.8 for RP and RP+, 8.7 and 6 for METR and METR+, 4.5 and 6.1 for KET and KET+, 0.8 and 2.4 for DA and DA+, 1.7 and 2.7 for TWIN and TWIN+, and 3.6 and 1.8 for MAST and MAST+. Setting the healthy cows as the referent, the 120 DIM hazard ratios (HR) for culling were MD 2.1, MD+ 2.9, MF 3.3, MF+ 4.6, RP+ 2.7, METR+ 1.8, KET 2.6, KET+ 3.3, DA 5.5, DA+ 4.5, TWIN 2.8, TWIN+ 3.0, MAST 3.1, and MAST+ 2.3. According to both decision tree and random forest analyses, MF was the most significant disease influencing survival, followed by DA, MAST, METR, and TWIN. In conclusion, the presence of MD or MD+ during the transition period was associated with increased culling risk in the first 120 DIM. The culling hazard was greater when an MD was complicated with another MD. In this study performed in a well-managed large farm, uncomplicated cases of RP (HR = 1.2) and METR (HR = 0.7) did not have an influence on the 120 DIM culling risk. Interestingly, both decision tree and random forest analyses pointed to MF and DA as main culling reasons in the first 120 DIM in the present dairy herd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".