Randomized single versus double embryo transfer: obstetric and paediatric outcome and a cost-effectiveness analysis
Bibliographic record
Abstract
BACKGROUND: Transfer of several embryos after IVF results in a high multiple birth rate associated with increased morbidity and high costs for the neonatal care. In a previous randomized trial we demonstrated that a single embryo transfer (SET) strategy, including one fresh single embryo transfer and, if no live birth, one additional frozen-thawed SET, resulted in a live-birth rate that was not substantially lower than after double embryo transfer (DET) but markedly reduced the multiple birth rate. METHODS: We compared costs for maternal health care and productivity losses and paediatric costs for the SET and DET strategies. In addition, maternal and paediatric outcomes between the two groups were compared. RESULTS: The SET strategy resulted in lower average total costs from treatment until 6 months after delivery. There were a few more deliveries with at least one live-born child in the DET group. The incremental cost per extra delivery in the DET alternative was high, 71 940. The rates of prematurely born and low birthweight children were significantly lower with the SET strategy. There were also markedly fewer maternal and paediatric complications in the SET group. CONCLUSIONS: The SET strategy is superior to the DET strategy, when number of deliveries with at least one live-born child, incremental cost-effectiveness ratio and maternal and paediatric complications are taken into consideration. The findings do not support continuing transfers of two embryos in this group of patients.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".