The Use of Pantoprazole Prior to Frozen Embryo Transfers - Clinical Trial
Bibliographic record
Abstract
In-vitro fertilization (IVF) is a common infertility treatment involving the retrieval and fertilization of mature eggs1. Embryos from an IVF cycle or donor can be cryopreserved (frozen) to be used in a future pregnancy through a process known as frozen embryo transfer (FET). The frozen embryo is thawed just prior to transferring into the uterus to initiate pregnancy2. Preterm birth, occurring before 37 weeks of gestation, is the leading cause of perinatal and neonatal morbidity and mortality globally3. Other critical complications that can occur during pregnancy include ectopic pregnancy and miscarriage. However, IVF is associated with a higher incidence of preterm birth, ectopic pregnancy, and miscarriage compared to natural conception3.Tocolytics, or uterine relaxant drugs, are often prescribed to address preterm birth4, but their application is limited due to adverse effects and short delivery delays5. To address these limitations, new therapeutic agents have emerged. One common approach in these investigations is to repurpose existing drugs with demonstrated safety, such as proton-pump inhibitors (PPIs), which act as effective relaxants of the myometrium via the Rho/ROCK pathway6. Pantoprazole, an FDA Category B and Health Canada-approved PPI, is safe for pregnancy use to reduce gastroesophageal reflux disease (GERD)-related symptoms7. Meta-analysis findings highlighted that PPI use during pregnancy demonstrated no increased risk for preterm delivery, spontaneous abortions, or major congenital birth defects8. Unfortunately, there is a gap in existing research, as few other studies investigate the use of PPIs during pregnancy and in conjunction with FET and IVF. This study will examine live birth rates and the presence/absence of pregnancy complications following the administration of pantoprazole during the time of FET.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".