P-026 The hCG Timing Myth in Insemination Cycles: The Surprising Truth About Pregnancy Rates
Bibliographic record
Abstract
Abstract Study question How does the timing of intrauterine insemination (D0, D1, or D2 post-hCG trigger) affect pregnancy rates, and what key factors influence these outcomes? Summary answer Overall pregnancy rates were similar across timing groups. Higher rates were observed in subgroups defined by specific follicle sizes, sperm concentration, or endometrial thickness. What is known already Intrauterine insemination (IUI) is a widely used assisted reproductive technique, valued for its simplicity, affordability, and accessibility. However, its success rates remain modest. Clinical guidelines recommend performing IUI within 24–36 hours following ovulation triggering with human chorionic gonadotropin (hCG). Yet, logistical challenges, such as weekend closures at medical centers, often disrupt adherence to these timelines. While some studies suggest that moderate deviations in timing may not significantly affect outcomes, robust evidence for establishing standardized practices remains limited. Optimizing IUI timing is therefore critical to improve success rates while addressing the logistical barriers faced by both healthcare facilities and patients. Study design, size, duration This retrospective observational study was conducted at Ovo Clinic, a university-affiliated private fertility center in Montreal, Canada, analyzing 1,614 IUI cycles performed between May and September 2024. The primary objective was to evaluate pregnancy rates based on insemination timing: D0 (day of trigger), D1 (24 hours post-trigger), and D2 (36 hours post-trigger). The timing of the insemination was chosen to avoid weekends and to spread out the clinic’s activity. Participants/materials, setting, methods IUI cycles were categorized by insemination timing (D0, D1, D2). The primary outcome was the pregnancy rate (PR), defined as a positive pregnancy test 14 days post-IUI. Data collected included maternal age, post-wash motile sperm concentration, endometrial thickness, and follicle size at induction. An analysis was conducted to assess the impact of these variables on PR, aiming to determine the optimal timing for insemination. Main results and the role of chance Mean maternal age was 34 years, comparable across groups. While post-wash motile sperm concentration was lower in the D0 group (D0: 79.1 vs. D1: 91.6 vs. D2: 100.7 M/mL), pregnancy rates were similar across timing groups: D0 (15.5%), D1 (9.8%), and D2 (12.6%) (p = not significant, NS). Higher PRs were observed in the D0 group when dominant follicle size measured 17-19mm (25%) and 20–22 mm (19.4%), endometrial thickness ranged from 7-10mm (18.4%) and 10–13 mm (16.4%), and post-wash motile sperm concentration exceeded 50 M/mL (20%). Notably, no pregnancies were recorded with endometrial thickness > 13mm or a post-wash motile sperm concentration <4 M/mL. Limitations, reasons for caution This study is limited by small sample sizes in certain subgroups, particularly for D0 cycles. Moreover, our results are based on pregnancy rates, which hold less clinical value compared to live birth rates. Larger prospective studies are required to confirm findings. Wider implications of the findings These findings support flexibility in IUI timing without compromising outcomes. Additionally, this study provides valuable insights into the optimal conditions—such as follicle size, endometrial thickness, and sperm concentration—that can enhance pregnancy rates. Trial registration number No
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".