Impact of Severe Male Factor Infertility on <i>In Vitro</i> Fertilization-Intracytoplasmic Sperm Injection Outcomes: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Severe male factor infertility presents challenges in assisted reproductive technology (ART). This study evaluated whether severe male factor infertility impacts clinical pregnancy outcomes following in vitro fertilization-intracytoplasmic sperm injection (IVF-ICSI) compared to non-male factor cases. Methods: In this retrospective cohort study at the Department of Reproductive Medicine and Surgery, SAT Hospital, Government Medical College, Thiruvananthapuram, couples undergoing IVF-ICSI between January 2017 and December 2020 were divided into severe male factor (group 1, n = 51) and non-male factor (group 2, n = 51) groups, matched for female age, body mass index, and ovarian reserve. Outcomes compared included clinical pregnancy rates (CPRs) based on sperm source (ejaculated vs. surgically retrieved) and male factor type (severe oligoasthenoteratozoospermia vs. azoospermia). Statistical analysis utilized univariate and multivariate logistic regression. Results: The overall CPR was 75.5%. Group 1 (severe male factor) achieved a CPR of 68.6%, while group 2 (non-male factor) achieved 82.4%, a difference that was not statistically significant (P = 0.107). Within group 1, no significant differences were found between outcomes using ejaculated versus surgically retrieved sperm (P = 0.393). Logistic regression showed lower odds of pregnancy with severe male factor infertility (odds ratio 0.469, 95% confidence interval: 0.185 - 1.190), but this was not statistically significant (P = 0.111). Conclusion: Severe male factor infertility, including cases requiring surgical sperm retrieval, does not significantly compromise CPRs in IVF-ICSI cycles when female factors and treatment protocols are optimized.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".