Effect of Exogenous Progesterone or Flunixin Meglumine After AI on Serum Progesterone Concentration and Pregnancy per AI in Lactating Dairy Cows
Bibliographic record
Abstract
The objective of this study was to determine the effect of post AI administration of exogenous progesterone (P 4 ) or a prostaglandin F2α (PGF2α) synthesis inhibitor agent on serum P 4 concentrations and pregnancy per AI (P/AI) in lactating dairy cows.Eighty lactating cows were randomly allocated to one of four treatment groups: 1) CON (control), received 5 mL of saline solution on d 6 and 14 post AI; 2) IP4 (injection of P 4 ), received 125 mg of P 4 im on d 6 and 14 post AI; 3) CIDR, received a controlled internal drug release insert containing 1.38g of P 4 from d 6 to 20 post AI; and 4) FM (Flunixin Meglumine), received 0.625 g of Flunixin Meglumine, a nonsteroidal anti-inflammatory drug, twice daily on d 15 and 19 post AI.Blood samples were taken on d 0, 6, 14, 17 and 20 post AI to determine P 4 concentrations.Transrectal palpation was performed between 40 and 45 d post AI to determine pregnancy status.All treatment groups (i.e.IP4, CIDR and FM) resulted in greater serum P 4 concentration on d 17 and 20 post AI compared to CON (P < 0.05).Cows given a CIDR insert had greater concentrations of P 4 on d 17 and 20 than IP4 and FM cows (P < 0.05).However, no significant difference was found between IP4 and FM groups for serum P 4 concentrations.The P/AI was greater (P < 0.05) in CIDR-treated cows (55%, 11/20) than CON (25%, 5/20), and intermediate in IP4 (40%, 8/20) and FM (35%, 7/20) cows.In summary, treatment with exogenous P 4 (i.e.CIDR and IP4) or FM increased serum P 4 concentrations in lactating dairy cows.However, results suggest that only CIDR administration would improve P/AI.
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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 | 0.001 |
| 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".