Value of ovarian reserve testing before IVF: a clinical decision analysis
Bibliographic record
Abstract
BACKGROUND: To assess the value of testing for ovarian reserve prior to a first cycle IVF incorporating patient and doctor valuation of mismatches between test results and treatment outcome. METHODS: A decision model was developed for couples who were considering participation in an IVF programme. Three strategies were evaluated: (I) withholding IVF without prior testing, (II) testing for ovarian reserve, and then deciding on IVF treatment if ovarian reserve was estimated to be sufficient, and (III) treatment with IVF without prior ovarian reserve testing. The outcome considered was the birth of a child. The valuation of the combination of the strategy conducted and the outcome accomplished was expressed on a distress scale in units of 'IVF cycles that were performed in vain'. Correct treatment with IVF and correct withholding of IVF were considered to bring no distress. The distress of withholding IVF in case pregnancy occurred is consequently specified by the ratio of the expected distress after incorrect withholding IVF to the expected distress after incorrect performing IVF (distress ratio). We interviewed both patients and doctors to determine realistic estimates for this distress ratio. RESULTS: The value of testing for ovarian reserve depends strongly on the expected pregnancy rate after IVF as well as on the valuation of the incorrect decisions from testing. For realistic ranges of the success rate after IVF and for distress ranges as were measured, treatment of all couples without testing was found to generate less distress than testing for ovarian reserve. The sensitivity and specificity of testing for ovarian reserve has to improve to 50 and 96% respectively, to make testing a valuable strategy. CONCLUSION: Based on the decision analysis, where current test accuracy and preference inventory among patients and physicians were used, testing for ovarian reserve seems not useful for current IVF programmes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".