Fetal Surveillance in High-Risk Fetal Cardiac Disease: Frequency, Results and Relationship with Survival
Bibliographic record
Abstract
Objectives We hypothesized that pregnancies with high-risk fetal heart disease (FHD) would benefit from frequent prenatal surveillance, abnormal fetal surveillance results would be associated with worse outcomes, and cardiovascular profile scoring (CVPS) could identify FHD cases at the highest risk of death. Methods A retrospective single-centre study of all actively treated pregnancies with high-risk FHD from 2006–2020. Frequency and results of fetal surveillance, survival, and mode of delivery were collected. Frequent fetal surveillance was defined as ≥ once weekly fetal well-being testing commencing by 28 0 –32 0 weeks of gestation, or from later diagnosis, until delivery. Where possible, the CVPS was calculated. Results Fetal surveillance results were available in 92% (56/61) of pregnancies with high-risk FHD and were abnormal in 18% (10/56). A final CVPS of ≤7 carried a higher mortality (11/21, 52%) than ≥8 (6/31, 19% ( P = 0.01)). There was a trend towards worse survival at the last follow-up when fetal surveillance was abnormal versus normal (40%, 4/10 vs. 72%, 33/46, P = 0.07). Survival did not differ between frequent versus infrequent surveillance (13%, 4/30 vs. 3%, 1/31, P = 0.20); nor when comparing abnormal versus normal surveillance results (20%, 2/10 vs. 7%, 3/46, P = 0.21). Where fetal surveillance was abnormal, emergency cesarean delivery was more common (70%, 7/10 vs. 4%, 2/46, P < 0.001). Conclusions Abnormal fetal surveillance results and/or a CVPS ≤7 may identify compromised fetuses with high-risk FHD who could benefit from altered management or expedited delivery. Given the high rates of abnormal fetal surveillance in high-risk FHD, frequent fetal surveillance in the third trimester should be considered.
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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 | 0.002 | 0.010 |
| 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 | 0.000 |
| 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".