Effect of tolfenamic acid in postpartum gilts and the performance of their piglets
Bibliographic record
Abstract
Postpartum dysgalactia syndrome (PPDS) is a common disorder affecting sows in intensive production systems. In most cases, hypogalactia is not clearly identified and assumes a subclinical aspect. Therefore, the present study aimed to evaluate the effect of a nonsteroidal anti-inflammatory drug (NSAID) based on tolfenamic acid as a prophylactic treatment for PPDS and the performance of suckling piglets. Gilts (n = 319) were randomly divided into two groups: a tolfenamic acid group (n = 157) and a control (n = 162). The tolfenamic acid group received a single intramuscular injection (1 ml/20 kg of 4% tolfenamic acid) after farrowing, whereas the control group received no treatment. The occurrence of PPDS was confirmed. All piglets (n = 4,466) were weighed at 1, 4, and 18 days of age. All litters were evaluated for weight gain, the occurrence of diarrhea, and mortality between 4 and 18 days of age. PPDS variables were analyzed using logistic regression. Piglet weights were analyzed based on covariance while considering the effects of initial weight and the presence of diarrhea. Tolfenamic acid had no significant effect on the incidence of PPDS. The tolfenamic acid group had a 0.41% lower piglet mortality rate until 18 days of age. Tolfenamic acid administered prophylactically to gilts after farrowing reduced piglet mortality during lactation and promoted weight gain.
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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 | 0.000 |
| 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".