Total Arch vs Hemiarch Repair in Acute Type A Aortic Dissection: Systematic Review and Meta-Analysis of Comparative Studies
Bibliographic record
Abstract
Background: We aimed to compare the short- and long-term outcomes of total arch replacement (TAR) vs hemiarch replacement (HAR) in the management of acute type A aortic dissection. Methods: We searched the literature for studies directly comparing TAR to HAR in acute type A aortic dissection. Hazard ratios (HRs) were extracted from digitized Kaplan-Meier curves. Results: A total of 6526 patients were identified, of which 2060 (32%) had received a TAR. A total of 37% of patients were female, and the mean age (standard deviation) of the cohort was 59.8 ± 11.8 years. TAR patients had a higher prevalence of preoperative malperfusion (34% vs 26%). The TAR group had higher odds of 30-day mortality (4404 patients; odds ratio [OR] 1.79, 95% confidence interval [CI] 1.29-2.49), renal failure requiring dialysis (3475 patients; OR 1.34, 95% CI 1.02-1.76), and a trend toward higher rates of stroke (3292 patients; OR 1.49, 95% CI 0.93-2.39). No significant differences were observed in prevalence of permanent spinal cord injury, visceral ischemia, or reoperation for bleeding. The TAR group had a non-statistically significant increase in long-term mortality (4408 patients; HR 1.25, 95% CI 0.99-1.57), but showed a trend toward improved freedom from long-term aortic reoperation (1359 patients; HR 0.53; 95% CI 0.18-1.59). In a subgroup analysis, the hazard ratio of long-term mortality favoured TAR in only the subgroup of studies in which the difference in malperfusion was > 10% between groups. Conclusions: TAR could be associated with improved freedom from long-term aortic reoperation but with potentially increased perioperative risks. We recommend a tailored surgical approach.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".