Twin–twin transfusion syndrome: a frequently missed diagnosis with important consequences
Bibliographic record
Abstract
OBJECTIVE: To evaluate the incidence and consequences of 'misdiagnosed' cases of twin-twin transfusion syndrome (TTTS). METHODS: Chorionicity and referral diagnoses were reviewed in pregnant women with monochorionic twin pregnancies complicated by TTTS treated with fetoscopic laser ablation. 'Misdiagnosed' cases, defined as failure to correctly identify chorionicity and/or to diagnose TTTS prior to referral, were compared with cases in whom chorionicity and TTTS were diagnosed correctly. TTTS stage, gestational age at referral, overall survival, fetal and perinatal mortality, gestational age at delivery, operating time and maternal complications were compared. RESULTS: Failure to identify monochorionicity and/or TTTS was observed in 33% (107/323) of referrals to our center. Compared with cases in whom chorionicity and TTTS were correctly diagnosed, misdiagnosed patients were referred at a more advanced stage of disease (Stage IV TTTS: 16.8% vs 7.9%, P = 0.014) and later in pregnancy (gestational age at laser: 20.9 weeks vs 20.1 weeks, P = 0.018). They also delivered more prematurely (30.3 weeks' gestation vs 31.5 weeks' gestation, P = 0.04) and fetal and neonatal mortality were higher (neonatal death within 7 days: 19.6% vs 6.0%, P < 0.001). When the diagnosis was incorrect, major maternal complications and intensive care unit admissions were increased. CONCLUSIONS: Poor recognition of chorionicity in the first trimester of pregnancy might lead to inadequate ultrasound follow up (failure to assess every 2 weeks) and patient education. Early accurate recognition of both chorionicity and TTTS, with timely referral to a fetal therapy center, are key to ensuring optimal maternal and fetal outcomes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".