Endometrial volume and thickness measurements predict pituitary suppression and non-suppression during IVF
Bibliographic record
Abstract
BACKGROUND: The aim of this prospective study was to evaluate the usefulness of three-dimensional (3D) ultrasound measurement of endometrial volume and thickness as a predictor of pituitary suppression and non-suppression following GnRH agonist administration for IVF. METHODS: A total of 144 women undergoing 164 IVF cycles had transvaginal ultrasound measurement of their endometrial volume and thickness following 8-14 days of buserelin acetate administration. Serum estradiol concentrations were measured on the same day. Receiver operating characteristic (ROC) curve analysis was used for statistics. A ROC curve was produced for each of four estradiol thresholds commonly used by clinics to diagnose pituitary suppression (100, 150, 200, 250 pmol/l). From each curve, endometrial volume and thickness thresholds that best predicted pituitary suppression and, separately, non-suppression were selected and the associated sensitivity, specificity, positive and negative predictive values were reported. RESULTS: The area under the curve (AUC) was consistently higher (better test) for 3D volume than thickness estimation for all four estradiol thresholds, although it was only significantly different when a threshold of 200 pmol/l was used. The AUC increased towards 1.0 (perfect test) for both volume and thickness measurement as the selected estradiol threshold increased. Very different volume and thickness thresholds were optimal depending on whether the aim of the test was to predict pituitary suppression or non-suppression. CONCLUSIONS: 3D endometrial volume estimation provides a new tool, alongside endometrial thickness measurement, to diagnose pituitary suppression and non-suppression during IVF. Different endometrial thresholds must be selected depending upon whether the priority is to identify pituitary suppressed, or arguably more importantly, non-suppressed cycles.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".