Cardiovascular magnetic resonance imaging predictors of pregnancy outcomes in women with coarctation of the aorta
Bibliographic record
Abstract
AIMS: The aim of this study was to determine associations between aortic morphometry evaluated by cardiovascular magnetic resonance (CMR) and pregnancy outcomes in women with aortic coarctation (CoA). METHODS: Consecutive women with CoA seen with CMR within 2 years of delivery were reviewed. Aortic dimensions were measured on CMR angiography. Adverse outcomes (cardiovascular, obstetric, and foetal/neonatal) were documented. RESULTS: We identified 28 women (4 with native and 24 with repaired CoA) who had 30 pregnancies. There were 29 live births (1 stillbirth) at mean gestation 38 ± 2 weeks. Mean maternal ages at first cardiac intervention and pregnancy were 6 ± 8 and 29 ± 6 years, respectively. There were nine cardiovascular events (hypertensive complications in five; stroke in two and arrhythmia in two) occurring in seven pregnancies. Minimum aortic dimensions were smaller in women with cardiovascular events (12.1 vs. 14.3 mm, P = 0.001), specifically in those with hypertensive complications (11.6 vs. 14.4 mm, P < 0.001). From receiver operator curve analysis, optimal discrimination for the development of adverse cardiovascular events occurred at the 12 mm diameter threshold [sensitivity 78%, specificity 91%, area under the curve 0.86 (95% CI: 0.685-1)]. All hypertensive events occurred in conjunction with a minimum aortic diameter of 12 mm (7mm/m(2)) or less. No adverse outcomes occurred if minimum diameter exceeded 15 mm. CONCLUSION: Smaller aortic dimensions relate to increased risk of hypertensive events in pregnant women with CoA. CMR can aid in stratification of risk for women with CoA who are considering pregnancy.
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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 | 0.004 |
| 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 | 0.001 |
| 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".