Regulatory control of gestagens use in Russia in the era of individualized luteal phase support for assisted reproduction cycles
Bibliographic record
Abstract
Approximately 12-18% of couples are infertile, according to WHO for the last 20 or 30 years. The most effective approach in the infertility treatment is assisted reproductive technology (ART). Despite the active development of this area and the constant improvement of the existing protocols, the gap between the pregnancy rate and the live birth rate reaches almost 30%. One of the serious difficulties standing in the way of the achieving a better reproductive outcome is the lag of the regulatory framework from the amount for some aspect’s elaboration, for example, the gestagens use for ART in Russia. The purpose of this review is to accumulate the latest data on the luteal phase support individualization in the post-transfer period based on biomarker — serum progesterone level, to analyze possible ways to overcome insurance and expert risks when using unregulated progesterone doses and dosage regimens, to emphasize the need for continuity in luteal phase support and pregnancy after ART to ensure the increase in the main criterion of ART effectiveness — live birth.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".