The effect of artificial oocyte activation on blastocysts rate in patients with low blastocyst rates: A retrospective cohort study
Bibliographic record
Abstract
Abstract Introduction Physiological oocyte activation requires a synergy between the oocyte and sperm to release calcium (Ca2+) through oscillations. The absence of such synergy between the oocyte and sperm leads to a negative impact on oocyte activation. Studies have shown that Artificial oocyte activation (AOA) is helpful in cases with failed or low fertilization rates. Studies present mixed opinions about increasing blastocyst rate. Methods A retrospective cohort single-center study was performed between January 2018 and October 2023, including 54 couples with suboptimal blastocyst development. The study compared intracytoplasmic sperm injection (ICSI) AOA cycles with previous conventional ICSI cycles and conventional ICSI without AOA cycles with previous conventional ICSI cycles in couples with failed or low blastocyst rates (< 30%) in the original ICSI cycle. Results We compared 22 AOA cycles to previous conventional ICSI cycles in the same patients and 32 conventional ICSI cycles without AOA to previous conventional ICSI cycles in the same patients. After AOA, the blastocyst rate was not significantly higher than the control group (48% vs 29% p=0.19). Conversely, the blastocyst rate was significantly higher in the conventional ICSI without AOA cycles than in the control group (48% vs 24% p=0.04). The fertilization rate was not statistically significant between the first and second cycles in both groups. Conclusion The literature still lacks strong evidence for AOA overcoming impaired embryonic development. Therefore, AOA remains reserved for couples with a failed or low fertilization history to improve fertilization results. Optimal laboratory conditions and ovarian stimulation modifications without AOA may improve blastocyst rates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".