Is Partially Thrombosed False Lumen Really a Predictor for Adverse Events in Uncomplicated Type B Aortic Dissection: A Systematic Review and Meta-Analysis?
Bibliographic record
Abstract
Objective: This meta-analysis and systematic review investigated whether partial thrombosed false lumen was a predictor for adverse events in uncomplicated Type B aortic dissection (TBAD). Methods: We performed the current systematic review of the medical literature according to the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The Newcastle-Ottawa Scale was used to evaluate the quality of individual studies. Search terms based on the MEDLINE database included “type B aortic dissection,” “false lumen” and “thrombosis.” The primary outcomes included mortality, intervention, and aortic growth. Results: Six studies were included in this systematic review, with a total number of 692 patients, including 197 patency (28.5%), 214 partial thrombosis (30.9%), and 281 complete thrombosis (40.6%). Due to the insufficient data for quantitative analysis, we only conducted a scoping review for mortality and intervention. For aortic growth, we conducted a meta-analysis based on Standardized Mean Difference (SMD). The SMD of PT vs. P by random effect model was −0.05 (random effect model) [95% confidence interval (CI), −0.39 to 0.29]. The 95% CI crossed with the null line of 0, indicating no significant difference. The SMD was 0.37 (fixed effects model) (95% CI, 0.03–0.71) and 0.70 (fixed effects model) (95% CI, 0.37–1.04) for PT vs. CT, and P vs. CT, respectively. Conclusions: Current researches on partial thrombosis of TBAD are inconsistent. Partial thrombosis is not associated with a faster aortic growth rate. Until more solid evidence is available, we do not recommend partial thrombosis as a surgical indication or high-risk profile for TBAD. Systematic Review Registration: Unique Identifier: CRD42019121912 .
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.049 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".