Pregnancy rates unaffected by sperm count in intrauterine insemination: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to elucidate the impact of both pre and postprocessing total motile sperm count (TMSC) on pregnancy rates in a subfertile population undergoing intrauterine insemination (IUI). METHODS: Subfertile couples presenting to the Stanford University Fertility Center during a two-year period were retrospectively enrolled. Eligible couples consisted of women with good ovarian reserve, proven tubal patency, normal anatomy and inducible ovulation. Ovulation induction was administered per standard protocols. IUI was performed using only fresh semen; samples were analyzed pre and post-processing. Pregnancy was established using β-HCG assays performed 15-17 days after IUI. Pregnancy rates for subgroups of pre and postprocessing TMSC were compared. RESULTS: A total of 981 couples underwent 2231 IUI cycles. Overall, the pregnancy rate was 20.2%. Pregnancy rates did not differ and remained rather stable for the pre (P=0.12) and post (P=0.66) processing semen analysis when stratified for TMSC. CONCLUSIONS: In the absence of teratospermia, TMSC does not appear to impact pregnancy rates in subfertile couples undergoing IUI.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".