Estimation of follicular growth—widely used, seldom studied
Bibliographic record
Abstract
We assessed whether estimation of follicular growth, rather than actual measurement of follicular size on the day of hCG trigger, affected pregnancy rates in intrauterine insemination (IUI) cycles. Patient and cycle characteristics were extracted from an existing database. Comparisons were made between the pregnant (defined as a positive beta hCG) and non-pregnant groups for the following variables: patient's age, number of previous IUI cycles, type of ovarian stimulation, endometrial thickness, number of follicles measuring 14 mm and above, pre and post wash sperm parameters, cycle day when IUI was done and number of days between last ultrasound scan and ovulation trigger. A total of 7302 cycles were included in the final analysis. In 4055 cycles (55.5%) the hCG trigger was on the day of the last ultrasound, in 2285 cycles (31.3%) the hCG trigger was 1 day after the last ultrasound, in 850 (11.6%) it was 2 days after the last ultrasound and in 112 (1.5%) it was 3 or more days after the last ultrasound. Sperm parameters, younger maternal age, and the number of follicles above 14 mm were all associated with pregnancy. No association was found between positive pregnancy test rates and the time from last ultrasound to hCG trigger. Planning IUI based on the estimation of follicular growth 1-4 days before trigger, does not affect pregnancy rates.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".