Cardiovascular magnetic resonance in pregnancy: Insights from the cardiac hemodynamic imaging and remodeling in pregnancy (CHIRP) study
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease in pregnancy is the leading cause of maternal mortality in North America. Although transthoracic echocardiography (TTE) is the most widely used imaging modality for the assessment of cardiovascular function during pregnancy, little is known on the role of cardiovascular magnetic resonance (CMR). The objective of the Cardiac Hemodynamic Imaging and Remodeling in Pregnancy (CHIRP) study was to compare TTE and CMR in the non-invasive assessment of maternal cardiac remodeling during the peripartum period. METHODS: Between 2010-2012, healthy pregnant women aged 18 to 35 years were prospectively enrolled. All women underwent TTE and CMR during the third trimester and at least 3 months postpartum (surrogate for non-pregnant state). RESULTS: The study population included a total of 34 women (mean age 29 ± 3 years). During the third trimester, TTE and CMR demonstrated an increase in left ventricular end-diastolic volume from 95 ± 11 mL to 115 ± 14 mL and 98 ± 6 mL to 125 ± 5 mL, respectively (p<0.05). By TTE and CMR, there was also an increase in left ventricular (LV) mass during pregnancy from 111 ± 10 g to 163 ± 11 g and 121 ± 5 g to 179 ± 5 g, respectively (p<0.05). Although there was good correlation between both imaging modalities for LV mass, stroke volume, and cardiac output, the values were consistently underestimated by TTE. CONCLUSION: This CMR study provides reference values for cardiac indices during normal pregnancy and the postpartum state.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".