Computed Tomography Imaging Measurements as a Surrogate for Clinical Outcomes After Surgical Management of Acute Type A Aortic Dissection: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Postoperative computed tomography imaging surveillance is an essential component of care after acute type A aortic dissection (ATAAD) repair. Prognostic imaging factors after ATAAD repair have not been systematically reviewed. METHODS AND RESULTS: We performed a systematic review to summarize postoperative computed tomography measurements including aortic diameter, cross-sectional area, volume, growth rate, and false lumen thrombosis in addition mid- to long-term clinical outcomes after ATAAD repair. Searches were conducted in Medline, Embase, and CENTRAL in October 2022. Studies were included if they reported clinical outcomes such as mortality or aortic reintervention after 1 year and included aforementioned computed tomography findings. Studies of chronic aortic dissection and studies of exclusive patient populations such as those with connective tissue diseases were excluded. Risk of bias was assessed with the Newcastle-Ottawa Scale. Searches retrieved 6999 articles. Sixty-eight studies met inclusion criteria (7885 patients). Extended repairs were associated with improved false lumen thrombosis, decreased aortic growth rate, and decreased rates of reintervention but not improved survival. Growth rates of the aorta post-ATAAD repair were highest in the descending thoracic aorta. The most frequent prognostic imaging factors reported were a patent/partially thrombosed false lumen and postoperative aortic diameter >40 to 45 mm. CONCLUSIONS: Established measurements of positive aortic remodeling, including complete false lumen thrombosis and stabilization of postoperative aortic diameter and growth are the most studied prognostic indicators for improved clinical outcomes after ATAAD repair. Growth rate of the aorta remains significant after ATAAD repair. Future studies should prospectively evaluate and compare prognostic factors for improved surveillance and management.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".