EP10.15: Prediction of dual twin survival after laser for Twin–twin transfusion syndrome
Bibliographic record
Abstract
We evaluated the predictive value of sonographic parameters at diagnosis of Twin–twin transfusion syndrome (TTTS) treated with Fetoscopic laser photocoagulation (FLPC) for postnatal dual twin survival. Additionally, we sought to validate Krispin et al's prediction model (UOG 2023). This is a retrospective cohort study of cases of TTTS treated by FLPC. The primary outcome was dual survival 30 days after delivery. The calculator used the following preoperative variables: donor's estimated fetal weight <10th centile, intertwin growth discordance >25%, anterior placenta, pulsatility index (PI) in the umbilical artery, ductus venosus and middle cerebral artery (MCA), with scores ranging 0-300. Among 157 patients, 84 (53.5%) had dual twin survival at 30 days, compared to 73 (46.5%) with one or no survivors. No significant differences in demographic parameters were observed between groups. There was no significant difference regarding the donor's EFW <10th centile (57.1% vs. 57.5% p = 0.96), and intertwin growth discordance >25% (26.2% vs. 38.4%, p = 0.95). However, anterior placenta was associated with lower survival (38.1% vs. 58.9%, p = 0.009). Dual survival had lower rates of PI >95th centile in the donor's umbilical artery and ductus venosus, and lower rates of decreased PI <5th centile in the donor's MCA. The observed dual survival was similar to the one predicted by the calculator for lower scores, but survival was higher for scores ≥100. The calculator predicted accurately the dual survival, except for advanced scores. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".