Contemporary management of pregnancy-related coronary artery dissection: A single-centre experience and literature review.
Bibliographic record
Abstract
Spontaneous coronary artery dissection (SCAD) is an infrequent event that is most commonly associated with pregnant women or those in the postpartum period. Because of its rarity, the literature describing this condition is confined to sporadic case reports, with few reporting long-term follow-up, and no clear consensus exists on the optimal treatment strategy for these patients. The present article reports a single-centre experience with SCAD, highlighting the issues surrounding its management with a brief description of five cases of pregnancy-associated coronary dissection. The treatment used in these cases ranged from a conservative medical approach to surgical and percutaneous intervention, with one patient proceeding to transplantation. Four of the cases have long-term angiographic follow-up.In addition, a comprehensive review of all previously published cases is presented, and temporal trends in the management strategy are highlighted. Possible pathophysiological mechanisms pertaining to this condition, and the complex diagnostic and therapeutic issues involved, which may affect both patient and fetus, are discussed. Finally, an optimal approach to patients with SCAD, informed by our experience and literature review, is described.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".