WS03: Infertility WS03‐01Selection of candidates for IVF based on color Doppler findings
Bibliographic record
Abstract
In the last 20 years there have been numerous improvements in IVF techniques leading to an exponetial increase in pregnancy rates. In spite of these improvements the patients undergoing IVF procedure still have more chance of failure than success. This presentation will address the areas in IVF treatment in which the use of color Doppler ultrasound could improve the results of IVF. Discussion will include the appropriate assessment of ovarian reserve as well as why the determination of the initial dose of gonadotropins based solely upon the patient's chronological age or serum FSH or E2 concentrations is imprecise. Recently, baseline ultrasound scans to assess for the presence of polycystic ovaries, and the use of color Doppler sonography to assess stromal perfusion have emerged as valuable tools in the assessment of the optimal dose selection of gonadotropins required for IVF ovarian stimulation. The issue of the assessment of oocyte maturity will additionally be discussed. Traditionally, follicular size has been used to assess oocyte maturity. However, frequently very poor oocytes are obtained from optimally grown follicles. It has been suggested that the assessment of peri‐follicular flow could potentially distinguish oocyte maturity in a superior manner than size of follicles. Finally, the value of measurement of endometrial thickness and its structure, as well as uterine and subendometrial flow in the assessment of endometrial receptivity will be discussed.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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".