241 Metoclopramide induces low-level hyperprolactinemia to increase milk production in sows
Bibliographic record
Abstract
Abstract Inadequate milk production by sows often limits the growth rate of piglets. A successful lactation requires prolactin (PRL)-induced differentiation of the alveolar epithelium within the mammary glands of sows between gestation days 90–110 (G90-110). We hypothesized that late gestational hyperprolactinemia in primiparous sows induced by feeding the dopamine antagonist metoclopramide (MET) would enhance mammary epithelial differentiation, milk yield, and piglet growth rate, and that these effects would carry over into a subsequent lactation. Twenty-six gilts were randomly assigned to receive either MET (n = 13, 0.8mg/kg) or vehicle (CON, n = 13) twice daily from G90-110. On G90, circulating PRL levels peaked 45 min after MET dosing (P < 0.001) then returned to baseline 3 h later. This response occurred daily out to G104 (P < 0.05). Treated gilts had increased alveolar lumen diameter on G110 (P < 0.05), though feed intake, body weight, and carcass fat percent were not different during pregnancy or lactation. Piglets born to treated sows had increased body weight and average daily gain on lactation days 14 and 21 (P < 0.05). Piglet milk intake was estimated from deuterium oxide dilution. While milk intake by piglets nursing MET sows was not statistically different from those nursing CON sows on day 21 of lactation (P = 0.18), there was a greater increase in milk consumption by piglets born to MET-treated sows between days 9 and 21 of lactation than for those in CON litters (P < 0.001). In one group of second parity sows, milk yield was increased during their second lactation with no additional treatment (P < 0.05) and their carcass fat dropped by 14% (P < 0.05). These findings demonstrate that MET-induced hyperprolactinemia in pigs during late pregnancy can increase milk yield and piglet growth rate, and this response can be sustained into a second lactation in some sows.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".