The Major Factors Affecting Oocyte Quality in IVF Cycles: A Narrative Review
Bibliographic record
Abstract
Background: The role of assisted reproductive technologies, including in-vitro fertilization [IVF], is increasing daily because of the significant rise in subfertility cases. Among women, the most observed causes of infertility are primary ovarian insufficiency and premature ovarian failure, accounting for 25% of cases, followed by tubal damage (20%) and uterine abnormalities (10%), all contributing to the increase in IVF cases among couples. The success of IVF depends on various factors; however, the role of oocyte quality and maturation level is considered a pivotal cardinal factor for the success rates of IVF. Methods: A thorough literature analysis was performed using the following search terms: “Oocyte Quality” and “IVF cycle.” The databases searched included PubMed, Google Scholar, MEDLINE, Cochrane Library, and ResearchGate. Discussion: IVF success rates especially depend on oocyte quality and level of maturation. Several factors affecting these two factors include obesity, which increases O2 and H2O2 levels resulting in elevated endoplasmic reticulum [ER] stress; Polycystic Ovary Syndrome [PCOS]; age; endometriosis; cyclic nucleotides used in IVF; thalassemia major which is associated with lower ovarian reserve and increased redox activity malignancies, and anti-neoplastic drugs, which may contribute to premature ovarian insufficiency. Various treatment options were proposed to improve oocyte quality and maturation level, including growth hormone [GH] supplementation alongside ovarian supplementation, autologous mitochondrial transfer, luteal phase ovarian stimulation, administration of MI-Melatonin-Vitamin D3, Duphaston, and putrescine supplementation. Conclusion: With the rising number of subfertility cases, the importance of Assisted Reproductive Technologies [ART] is growing. The success rate of IVF on oocyte quality and level of maturation level, and few with several factors affecting these. Though numerous treatment options have been proposed to enhance oocyte quality and maturation, not all have been deemed beneficial.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".