Enhancing intrauterine insemination success in advanced maternal age: Impact of consecutive ejaculate and optimised cycle parameters
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it