Impact of Selective Fetal Growth Restriction on Laser Therapy Outcomes in Twin-Twin Transfusion Syndrome
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to determine if outcomes of fetoscopic laser photocoagulation in isolated twin-twin transfusion syndrome (TTTS) differ from TTTS with concomitant selective fetal growth restriction (sFGR). METHODS: This is a retrospective cohort study of all cases of TTTS treated at the CHU Sainte-Justine between February 2006 and January 2020. Data were collected from maternal, obstetrical, and neonatal chart review. RESULTS: A total of 149 patients were included in our study. Forty-seven patients (31.5%) had a pregnancy complicated by TTTS and sFGR. Mean gestational age at diagnosis and at treatment was 20+4 weeks and 20+6 weeks for TTTS alone, and 20+5 weeks and 21+2 weeks with concomitant sFGR. The presence of concomitant sFGR negatively impacted survival. Double survival in the TTTS + sFGR was 48.9% (23/47) versus 68.6% (70/102) in the TTTS-only group (p = 0.021). Fetal donor survival was 59.6% (28/47) in the TTTS + sFGR group and 84.3% (86/102) in the TTTS-only group (p = 0.001). However, the survival of at least one twin did not differ between the two groups: 93.6% (44/47) in the TTTS + sFGR group versus 92.2% (94/102) in the TTTS-only group (p = 0.751). The presence of type 2-3 sFGR (OR = 0.56; 95% CI 0.32-0.96, p = 0.033) and gestational age at laser therapy (OR = 1.17; 95% CI 1.01 = 1.36, p = 0.036) were independently associated with dual survival. CONCLUSION: sFGR is independently associated with decreased double survivorship at the expense of the donor in TTTS undergoing laser therapy. Type 2 or 3 sFGR and early gestational age at treatment are especially at risk. A larger cohort is needed to validate our results.
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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 | 0.000 | 0.000 |
| 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".