Effect of selective fetoscopic laser photocoagulation therapy for twin–twin transfusion syndrome on pulmonary valve pathology in recipient twins
Bibliographic record
Abstract
OBJECTIVE: To investigate the impact of selective fetoscopic laser photocoagulation (SFLP) on pre-existing pulmonary valve pathology in the recipient twin in twin-twin transfusion syndrome (TTTS). METHODS: We evaluated preoperative echocardiograms of all pregnancies with TTTS treated with SFLP at our institution from 2001 to 2009 (n = 76). Sixteen (21%) recipients had an abnormal pulmonary valve (stenosis/dysplasia, insufficiency or functional atresia) before SFLP. Postoperative echocardiograms and medical records from these 16 recipients were reviewed. Changes in pulmonary valve structure and function, and overall cardiac function, were noted after SFLP. RESULTS: The mean gestational age at SFLP was 21 (range, 18.7-24.3) weeks. Seven of sixteen (44%) recipients with abnormal pulmonary valve prior to SFLP survived. Six of the 16 (37.5%) recipient twins had documented absence of persistent pulmonary valve abnormalities at birth or at autopsy. Two (12.5%) of the 16 recipients (2.6% of the original cohort) had persistent pulmonary valve abnormalities at birth, requiring intervention. Systolic and diastolic function improved or normalized after SFLP in all patients undergoing longitudinal follow-up. There was a tendency for a better cardiovascular profile score (best = 10 points) at initial evaluation in pregnancies with survivors compared with those with no survivors (mean (SD): 5.6 (2.2) vs. 6.75 (1.28)), but this was not statistically significant. Severity of cardiac involvement did not predict persistence of valve pathology or survival. CONCLUSIONS: SFLP can improve flow through the pulmonary valve of the recipient twin in TTTS, probably as a consequence of improvements in right ventricular systolic and diastolic function. However, pulmonary valve pathology may persist and require postnatal intervention.
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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 | 0.002 |
| 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) | 0.000 | 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".