An Analysis of pre and post-Processing Semen Parameters at The Time of Intrauterine Insemination; and The Confounding Effects of Total Motile Sperm Counts on Pregnancy Outcome: A Prospective Cohort Study.
Bibliographic record
Abstract
BACKGROUND: This study aims to determine whether pre or post-processing semen parameters obtained during intrauterine insemination (IUI) predict pregnancy when controlling for confounding effects. MATERIALS AND METHODS: A prospective cohort study of 2231 semen analyses was conducted at McGill University of IVF center. Any couples who underwent IUI with partner sperm, over a 2.5-year period, were included. Controlled ovarian stimulation was done with Clomiphene Citrate, Letrozole, or Gonadotropins. Statistical analysis was performed using t tests, two types of stepwise logistic regression, and stepwise discriminant analysis. A comparison of pre and post-processing semen parameters was undertaken to determine the probability of pregnancy. RESULTS: There were significant differences between pregnant and non-pregnant women in post-processing concentration (P=0.043), post-processing total motile sperm count (TMSC) (P=0.049), and post-linearity (P=0.012). However, when variable out-of-the-equation logistic regression or discriminant analysis, which controls for confounding effects between variables, were used, the findings were no longer significant. It was statistically proven that when a variable in the equation logistic regression was employed, post-processing concentration (P=0.005) and post-processing TMSC (P=0.009) remained reliable predictors of pregnancy. CONCLUSION: Two of three prediction models suggested that TMSC's relationship with pregnancy is due to confounding factors. One model maintained the validity of the TMSC. While TMSC has always been studied as an important predictor of insemination pregnancies, this finding may be due to confounding effects between semen parameters and therefore requires further investigation as to this relationship.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".