Aortic dissection in pregnancy and the postpartum period
Bibliographic record
Abstract
Pregnancy-associated aortic dissection (AD) is a rare event, with an incidence of 0.0004% per pregnancy. The work of the Aortic Dissection Collaborative identified pregnancy-associated AD as a high-priority topic, despite its rarity. The Pregnancy Working Group, which included physicians and patient stakeholders, performed a systematic literature review of pregnancy-associated AD from 1960 to 2021 and identified 6,333 articles through PubMed, OVID MEDLINE, Cochrane, Embase, CINAHL and Web of Science. The inclusion criterion was AD in pregnant populations and exclusion criteria were case reports, conference abstracts, and languages other than English. Assessment of full-text articles for eligibility after removal of duplicates from all databases yielded 68 articles to be included in the final review. Topics included were timing of AD in pregnancy, type of AD, and management considerations of pregnancy-associated AD. The Pregnancy Working Group identified gaps in knowledge and future areas of research for pregnancy-associated AD, including clinical management, mental health outcomes post AD, reproductive and genetic counseling, and contraception after AD. Future collaborative projects could be a multicenter, international registry for all pregnancy-associated AD to refine the risk factors, best practice and management of AD in pregnancy. In addition, future mixed methodology studies may be useful to explore social, mental, and emotional factors related to pregnancy-associated AD and to determine support groups' effect on anxiety and depression related to these events in the pregnancy and postpartum period.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".