Pregnancy rate after treatment with a nonsteroidal anti-inflammatory drug (tolfenamic acid) at the time of embryo transfer in recipient cows
Bibliographic record
Abstract
Pregnancy rates following embryo transfer (ET) in cattle may be influenced by transient inflammatory responses triggered during the procedure. The objective of this study was to evaluate the effect of tolfenamic acid (TA), a nonsteroidal anti-inflammatory drug (NSAID), on pregnancy rates in embryo-recipient females. A total of 1431 recipients (367 heifers and 1064 cows) were enrolled in 34 ET programs conducted on 24 commercial farms in Uruguay, using either in vivo-derived or in vitro-produced embryos, fresh or frozen. At the time of ET, recipients were randomly assigned to either a treatment group receiving a single dose of TA (2 mg/kg intramuscularly) or a control group receiving no treatment. Pregnancy diagnosis was performed between days 35 and 60 post-ET via transrectal ultrasonography. No significant difference was attained in overall pregnancy rates between TA-treated (53.7 %, 389/725) and untreated (49.7 %, 351/706) recipients (P = 0.13). However, in specific subgroups TA treatment increased pregnancy rates, reaching statistical significance in heifers (P = 0.04) and a tendency in animals with a body condition score below 3.0 (1-5 scale; P = 0.07) and recipients with smaller corpus luteum size (P = 0.09). Additionally, NSAID treatment improved pregnancy rates in recipients receiving in vitro-produced embryos at the morula stage (P = 0.05) but not at the early to expanded blastocyst stages (P = NS). In conclusion, we suggest that TA may provide reproductive benefits under specific ET situations, particularly in heifers and recipients with physiological or embryonic conditions commonly associated with reduced fertility.
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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 | 0.001 | 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 | 0.001 |
| 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".