The efficacy of luteal phase support in women with polycystic ovary syndrome following assisted reproductive technology: a systematic review
Bibliographic record
Abstract
Abstract Background Polycystic ovary syndrome (PCOS) is a complex endocrine condition prevalent among a significant number of women during their reproductive years. Remarkably, 90–95% of women seeking infertility solutions due to anovulation are diagnosed with PCOS. Luteal phase support (LPS) is a crucial aspect of assisted reproductive technologies (ART). This systematic review aimed to evaluate the effectiveness of LPS in women with PCOS undergoing ART, with a focus on pregnancy rates as the primary endpoint. Materials and methods A systematic search was conducted on EMBASE, PubMed, and Scopus databases without language restrictions. We searched for studies up to August 1, 2023. The search strategy used terms related to PCOS and LPS. Clinical trials and cohort studies involving infertile women with PCOS undergoing ART were included. The Risk of Bias 2 (ROB2) and the Newcastle-Ottawa Scale (NOS) tool were used to assess the risk of bias. Results The review included five studies comprising a total of 818 patients. The studies used various ovulation induction medications, such as letrozole, clomiphene citrate, and human menopausal gonadotropin, in combination with different forms of progesterone for LPS (oral, intramuscular, and intravaginal). The overall results demonstrated inconsistent efficacy of LPS, with some studies showing significant improvements in pregnancy rates with LPS, while others showed no statistically significant difference. Conclusion The systematic review suggests that LPS may improve pregnancy rates in women with PCOS undergoing ART. However, the effectiveness appears to be influenced by the choice of ovulation induction agent and the route of progesterone administration. Personalized treatment approaches considering patient response and emerging evidence are essential.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".