Intracytoplasmic Sperm Injection – Factors Affecting Fertilization
Bibliographic record
Abstract
The ICSI has become method of choice to achieve fertilization. Fertilization is possible in cases in which the sperm motility and ability to penetrate the zona pellucida are impaired. Injection is possible with sperm obtained from ejaculation, microsurgical epididymal sperm aspiration (MESA), percutaneous epididymal sperm aspiration (PESA), or testicular sperm extraction (TESE). In addition, indications for ICSI include idiopathic infertility and repeated conventional in vitro fertilization (IVF) failures [1]. Fertilization rate after ICSI is at about 70 to 80% in all ages combined [2]. This suggests that, despite injecting sperm into mature oocytes, failed fertilization still occurs. Total failed fertilization (TFF) refers to failure of fertilization in all mature oocytes and “failed fertilization” refers to failure of fertilization in any mature oocyte. Based on a considerable emotional and financial involvement in a cycle of assisted reproduction, TFF is a distressful event for the infertile couple as well as the fertility professionals. TFF occurs in 5–10% of IVF [3] and 1-3% of ICSI cycles [4]. TFF after ICSI cycles is mostly due to low number of mature oocytes [4] or oocyte activation failure [5]. TFF is a rare event in cases with normal oocytes and sperm [6]. Some patients may face repeated TFF in spite of normal sperm parameters and good ovarian response [7]. In such cases, the primary reason for failed fertilization after ICSI is lack of oocyte activation, as more than 80% of these oocytes contain a sperm [4]. Considerable advances in artificial oocyte activation and recovery of sperm from epididymis or testis, suitable for ICSI, help avoid TFF. This chapter discusses the factors affecting success rate of ICSI, highlights causes of failure and suggests remedies for failed fertilization after clinical ICSI.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".