A nationwide evaluation of spontaneous coronary artery dissection in pregnancy and the puerperium
Bibliographic record
Abstract
OBJECTIVE: Spontaneous coronary artery dissection (SCAD) is a rare and potentially lethal cause of myocardial infarction (MI). The purpose of our study was to estimate the prevalence and maternal outcomes of pregnancies complicated by SCAD. MATERIALS AND METHODS: A population-based cohort study on all births identified in the Healthcare Cost and Utilization Project from 2008 to 2012. Disease prevalence was calculated and logistic regression was used to estimate the adjusted odds ratio (aOR) for risk factors and different maternal complications. RESULTS: A total of 4 363 343 pregnancy-related discharges were evaluated. 79 cases of SCAD were identified resulting in a prevalence of 1.81 per 100 000 pregnancies. The mean maternal age at the time of diagnosis was 33.4 years (±5.2). Chronic hypertension (aOR, 2.67; 95% CI 1.18 to 6.03), lipid profile abnormalities (aOR, 48.22; 95% CI 24.25 to 95.90), chronic depression (aOR, 3.56; 95% CI 1.43 to 8.83) and history of migraine (aOR, 3.93; 95% CI 1.52 to 10.17) were associated with an elevated risk for SCAD. MI was diagnosed in 66 (85.5%) cases of SCAD with anterior and subendocardial territories being the most common locations. Thirty one patients (40%) with SCAD underwent angioplasty with the majority receiving stents, which was associated with a longer hospital stay than those treated conservatively or with bypass. CONCLUSIONS: SCAD is a rare aetiology of MI; risk factors and outcomes are illustrated in the current study. The puerperium is an important period for the development of pregnancy-related SCAD. Careful evaluation of pregnant and postpartum women with chest pain is warranted, especially if these risk factors are identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".