Efficacy of intracytoplasmic sperm injection in women with non‐male factor infertility: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Introduction The use of intracytoplasmic sperm injection (ICSI) has dramatically increased in patients with non‐male factor infertility during the last decades. However, whether ICSI provides a significant benefit over in vitro fertilization (IVF) in these patients is still controversial. In this study, we aimed to investigate the efficacy of ICSI on reproductive outcomes with non‐male factor infertility and to provide updated evidence for clinical practice. Material and methods We searched Medline, Embase, and the Cochrane Central Register of Controlled Trials from inception to March 2023. Randomized controlled trials (RCTs) comparing the efficacy between ICSI and IVF in patients with non‐male factor infertility were included. The main outcomes were the live birth rate (LBR), fertilization rate (FR), and total fertilization failure (TFF). The pooled estimates were calculated using the random‐effects models as relative risk (RR) with 95% confidence intervals (CIs). This systematic review and meta‐analysis was registered in PROSPERO (CRD42023427004). Results We included 18 RCTs with 3249 cycles and 30 994 oocytes. The results demonstrated that ICSI reduced the risk of TFF (RR = 0.26, 95% CI: 0.13–0.50, I2 = 58%) and increased FR per oocyte inseminated/injected (RR = 1.14, 95% CI: 1.08–1.20, I2 = 69%), but it did not improve LBR (RR = 1.11, 95% CI: 0.94–1.30, I2 = 0%) or other outcomes compared with IVF. However, the difference in fertilization failure reduction between ICSI and IVF may be explained by different randomization methods (randomization based on patients vs. sibling oocytes). When considering only studies with randomization based on patients, we found no evidence of the difference between the groups (RR = 0.72, 95% CI: 0.48–1.06, I2 = 0%). Furthermore, no differences were observed in subgroup analyses based on other factors, including female age, study period, and controlled ovarian stimulation protocols. Conclusions Our findings suggest that ICSI leads to no difference in reproductive outcomes compared to IVF in patients with non‐male factor infertility. Considering the cost and safety of ICSI, we have no evidence to support the routine use of ICSI in these populations. High‐quality RCTs with large sample sizes will be needed to confirm our results and explore clinical and neonatal outcomes.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".