Does optimal follicular size in IUI cycles vary between clomiphene citrate and gonadotrophins treatments?
Bibliographic record
Abstract
OBJECTIVE: To evaluate pregnancy-related leading follicles during ovulation induction and superovulation with clomiphene citrate (CC) or gonadotropin. DESIGN: Retrospective cohort. PATIENTS: Five hundred and forty-two women who underwent a total of 615 treatment cycles with CC or gonadotropin. INTERVENTION: We evaluated the effects of CC and gonadotropin on the leading follicles, clinical pregnancy rates and miscarriage rate. RESULTS: The number of follicles larger than 15 mm in the different protocols was comparable. In those treated with CC, the diameter of the dominant follicles before human chorionic gonadotropins (hCG) trigger in the conception cycles (20.4 ± 1.2 mm) was significantly larger than in the non-conception cycles (18.8 ± 1.9 mm). In women treated with gonadotropin, the diameter of the leading follicle in the conception cycles (18.5 ± 1.7 mm) was comparable to that in the non-conception cycles (18.2 ± 1.7 mm). The pregnancy-related diameter of the leading follicle in CC cycles (20.4 ± 1.2 mm) was significantly larger than that in gonadotropin cycles (18.8 ± 1.9 mm; p = 0.001; 95% CI, -2.2 to -0.9). CONCLUSION: Pregnancy-related diameter of the leading follicle in CC cycles is significantly larger than that in gonadotropin cycles and the best time for hCG trigger in the CC cycle is when the leading follicle reaches 20 mm.
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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 | 0.009 |
| 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".