Impact of Lowering Dietary Cation-Anion Difference in Nonlactating Dairy Cows: A Meta-Analysis
Bibliographic record
Abstract
A meta-analysis of previous studies was performed to clarify the response of prepartum dairy cows to lowering dietary cation-anion difference (DCAD) and to compare different equations that have been proposed to calculate DCAD. Twenty-two published studies containing 75 treatment groups met criteria for inclusion in the meta-analysis. Five different equations used to calculate DCAD were compared for their association with clinical milk fever and urinary pH. The DCAD equation (Na + K) - (Cl + 0.6 S) was the most highly associated with clinical milk fever (R(2) = 0.44) and urinary pH (R(2) = 0.85). Lowering DCAD reduced clinical milk fever but also reduced DM intake. Lowered DCAD was associated with reduced urinary pH, blood bicarbonate, and blood CO(2), suggesting a metabolic acidosis with respiratory compensation. Blood pH was very slightly lowered by lowered DCAD. Lowering DCAD increased ionized Ca in blood before and at calving. The model predicted that lowering DCAD from +300 to 0 mEq/kg reduced risk for clinical milk fever from 16.4 to 3.2%, reduced urinary pH from about 8.1 to 7.0, and reduced DM intake by 11.3%.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.020 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".