Malperfusion Syndrome in the Setting of Type A Aortic Dissection
Bibliographic record
Abstract
Background: Acute Type A aortic dissection (TAAD) patients who experience malperfusion syndrome (MPS) are at the greatest risk of major morbidity and mortality. However, the appropriate timing for open proximal aortic repair in the presence of MPS is still uncertain due to variations in clinical manifestations and diverse treatment approaches.\nMethods: We conducted a comprehensive literature review to understand the importance of MPS in the setting of TAAD. Then, we conducted a national survey to understand the perception of cardiovascular surgeons across Canada and their approach for those patients. Furthermore, we performed a retrospective analysis of our local data to analyze the outcomes of TAAD with and without malperfusion syndrome from December 1999 to December 2021. Finally, we started a prospective pilot study to assess the feasibility and safety of using intravascular ultrasound (IVUS) to assess and early diagnose MPS in the setting of TAAD.\nResults:The mortality of TAAD ranges from 17% to 31%, with a third of patients presenting with MPS. Mortality of TAAD patients increases significantly in the presence of MPS Specialized centers across the world have adopted new approaches to address malperfusion syndrome and have reported improved outcomes. From the national survey that we conducted, there is awareness of the significance of malperfusion syndrome in the setting of TAAD among the cardiac surgeons across Canada. From our local institution, the 30-day in-hospital mortality of TAAD is 13.6%. The mortality of patients with malperfusion is 36.2%, while without malperfusion is 6.2% (p\nConclusion: Adopting new approaches to address malperfusion syndrome in the setting of TAAD is critical to improving mortality and morbidity outcomes.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".