Prediction of Dual Twin Survival after Laser for Twin-to-Twin Transfusion Syndrome
Bibliographic record
Abstract
INTRODUCTION: Twin-to-twin transfusion syndrome (TTTS) is associated with high perinatal morbidity and mortality. Krispin et al. [Ultrasound Obstet Gynecol. 2023;61(4):511-7] developed a prediction model to estimate the likelihood of dual twin survival after fetoscopic laser photocoagulation (FLPC). This study aimed to evaluate the predictive value of sonographic parameters at diagnosis of TTTS treated with FLPC for postnatal dual twin survival and to validate Krispin et al.'s calculator. METHODS: 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 preoperative variables: donor's estimated fetal weight (EFW) <10th centile, intertwin growth discordance >25%, anterior placenta, pulsatility index (PI) in the umbilical artery (UA), ductus venosus (DV), and middle cerebral artery (MCA), with scores ranging 0-300. RESULTS: Among 157 patients, 84 (53.5%) had dual twin survival (Group A), compared to 73 (46.5%) with one or no survivors (Group B). No significant differences were seen in donor's EFW <10th centile (57.1% [A] vs. 57.5% [B], p = 0.96), intertwin growth discordance (26.2% [A] vs. 38.4% [B] p = 0.95), rates of PI >95th centile in the donor's UA and DV, and PI <5th centile in the MCA (p > 0.05). However, a significant difference was found for anterior placenta (38.1% [A] vs. 58.9% [B], p = 0.009). The observed dual survival was higher than predicted for scores ≥100. CONCLUSION: We were not able to externally validate the calculator of dual survival after laser for TTTS, especially for elevated scores. Among the parameters analyzed, only anterior placenta was significantly associated with poorer outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".