Enhancing intrauterine insemination success in advanced maternal age: Impact of consecutive ejaculate and optimised cycle parameters
Bibliographic record
Abstract
OBJECTIVE: This study evaluated whether consecutive ejaculate (CE) strategies improve intrauterine insemination (IUI) live birth rates (LBR) in women over 35 with unexplained or male-factor infertility. It also examined the influence of follicle number and sperm count thresholds on outcomes. METHODS: In this retrospective cohort study (2010-2019), 596 IUI cycles were analysed in 263 nulliparous women-230 with CE and 366 with standard IUI. Among them, 98 patients underwent CE IUI and 165 received non-CE IUI. Patients with total motile sperm count (TMSC) <5×106 were often fast-tracked to IVF, but CE was mostly attempted to boost sperm count beforehand. LBRs per cycle and per woman were compared between groups. RESULTS: LBR per cycle was 11.3% (CE) vs. 13.1% (control) (p=0.52); per woman, 26.5% (CE) vs. 29.1% (control) (p=0.65). Mean ages were similar (37.7 vs. 38.0 years; p=0.34). Success improved with TMSC >10×106; 65.4% (CE) and 87.5% (control). Over six cycles, LBR rose from 10.5% to 13.8% (CE) and 12.3% to 16.7% (control). Outcomes improved with two or three follicles, especially in women over 35. CONCLUSIONS: CE IUI yields LBRs comparable to standard IUI and may offer a cost-effective, less invasive alternative to IVF for male-factor infertility in women over 35. The LBRs per woman undergoing IUI were of a similar magnitude to those reported in IVF cycles. Optimising IUI LBR may involve increasing follicle numbers and using a higher TMSC threshold (>10×106). CE IUI supports healthcare sustainability while expanding fertility treatment access.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".