Predicting pregnancy in women undergoing in-vitro fertilization with basal serum follicle stimulating hormone levels between 10.0 and 11.9 IU/L
Bibliographic record
Abstract
OBJECTIVE: To evaluate the results of the in vitro fertilization (IVF) cycle outcomes in women whose borderline basal follicle stimulating hormone (FSH) levels were between 10.0 and 11.9 IU/L and to analyze the predictors of pregnancy in this population. MATERIAL AND METHODS: A prospective cohort study was performed at an academic teaching hospital; participants were infertile couples in which the women were undergoing IVF treatment and had borderline basal highest FSH levels between 10.0 and 11.9 IU/L. Statistical modeling was performed to determine risk factors for pregnancy and clinical pregnancy. RESULTS: A clinical pregnancy rate of 26.5% per cycle and 35% per patient was found in the study population. Among all subjects and non-intracytoplasmic sperm injection (ICSI) subjects, younger age, higher gravidity, higher number of mature follicles on day of Human Chorionic gonadotrophin (hCG) triggering, higher number of oocytes retrieved, and number of embryos produced were significant discriminators between individuals who conceived and those who did not. However, only the number of embryos predicted those who had a clinical pregnancy when compared with those who did not. Higher gravidity, and basal estradiol (E2) levels, and lower maximum basal FSH levels predicted clinical pregnancy in non-ICSI patients. Among ICSI patients, the only predictor of pregnancy was a thicker endometrium. A trend towards higher pregnancy rates was noted in ICSI patients. CONCLUSION: We showed that pregnancy rates per cycle and per patient in this population were not significantly different than those in patients with a basal FSH level below 10.0 IU/L. Preliminary evidence suggests that ICSI is the fertilization method of choice in these patients.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".