The use of assisted reproductive technology before male factor infertility evaluation
Bibliographic record
Abstract
BACKGROUND: Some centers offer assisted reproductive technologies (ARTs) [intra-uterine insemination (IUI) and in-vitro fertilization (IVF)], to treat certain couples with male factor infertility without having the men assessed by male infertility specialists. We sought to compare characteristics of couples having or not having prior ART use. METHODS: We used our prospectively collected database to identify men undergoing an initial evaluation for male infertility between 1995-2017. We obtained data on patient demographics, use of IUI and IVF, and semen analysis parameters. We used multivariable logistic regression to identify characteristics associated with prior use of ART. RESULTS: One thousand and five hundred forty-five out of 8,962 (17.2%) men reported use of ARTs prior to evaluation. Of these, 258 tried both IUI and IVF. More than one attempt was reported in 470 (37.2%) and 154 (28.2%) of men with prior IUI and IVF, respectively. Younger male age [adjusted odds ratio (aOR) 0.97/year; 95% confidence interval (CI), 0.95 to 0.99], older female partner age (aOR 1.07/year; 95% CI, 1.04 to 1.10), and year of visit (aOR 1.05/year; 95% CI, 1.01 to 1.09) were significantly associated with prior IUI. Older female partner age (aOR 1.07/year; 95% CI, 1.02 to 1.12) was significantly associated with prior IVF, but not male age or year of visit. Semen analysis parameters were not associated with prior ART. CONCLUSIONS: The prior use of ART is common among men presenting for an initial evaluation at a male infertility specialty clinic. Older female partner age was associated with use of reproductive technologies prior to evaluation, however, semen analysis parameters were not.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".