What is the most relevant standard of success in assisted reproduction? The singleton, term gestation, live birth rate per cycle initiated: the BESST endpoint for assisted reproduction
Bibliographic record
Abstract
Assisted reproduction programmes do not report success consistently. Rates vary with the definition used. Success must reflect delivery of healthy babies, and the burden of treatment to couples. We report the singleton, term gestation, live birth rate of a baby per assisted reproductive technology cycle initiated for a large IVF programme. We defined assisted reproductive technology cycles as those initiated with the intention of oocyte collection. We examined cycles conducted through Monash IVF in 2001. All women with positive pregnancy tests had first trimester ultrasonography. Obstetric outcomes were recorded. All babies had neonatal examinations conducted by paediatricians. A total of 644 positive pregnancy tests were recorded in 2600 cycles; 509 showed fetal heart motion. Of 448 deliveries, 328 were singleton and 120 were multiple. There were 290 singleton deliveries at term gestation. In 2001, a couple had an 11.1% chance of delivering a singleton, term gestation, live baby per assisted reproductive technology cycle begun. We suggest that delivery of a single, term gestation, live baby per cycle initiated is the most relevant standard of success. This statistic was 11.1% at Monash IVF. We encourage programmes to report this BESST (Birth Emphasizing a Successful Singleton at Term) outcome.
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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 | 0.043 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".