Meta-analysis of open surgical repair versus hybrid arch repair for aortic arch aneurysm
Bibliographic record
Abstract
OBJECTIVE: To conduct a meta-analysis of available comparative studies evaluating hybrid arch repair versus open surgical repair of aortic arch aneurysm. METHODS: A literature search was performed using PubMed, Embase and Web of Science to identify any studies comparing the results of hybrid arch repair with open surgical repair of aortic arch aneurysm. Study quality was assessed with the Newcastle-Ottawa Scale. Statistical heterogeneity was estimated using the chi-square test. A random-effects model was used to illustrate heterogeneity. Publication bias was evaluated by funnel plots. RESULTS: Seven retrospective cohort studies from 2009 to 2016 comprising 727 patients were included. Among these patients, 269 were treated with hybrid arch repair and 458 with open surgical repair. There was no significant difference in operative mortality (OR 0.75; 95% CI 0.41-1.39; P = 0.37), permanent neurological deficit (OR 1.24; 95% CI 0.73-2.13; P = 0.42), late mortality (2 years) (OR 3.41; 95% CI 0.83-14.03; P = 0.09) or renal failure (OR 0.80; 95% CI 0.40-1.61; P = 0.53). Interestingly, the meta-analysis indicated that the hybrid group needed more reinterventions (OR 3.43; 95% CI 1.72-6.84; P = 0.0005). CONCLUSIONS: We found no strong evidence indicating that hybrid arch repair is superior to open surgical repair. Furthermore, the hybrid arch repair resulted in more reinterventions despite the fact that it was a less invasive procedure; it also required fewer days in the hospital. Further studies with large numbers of participants and long-term follow-ups are necessary to confirm the effectiveness of hybrid arch repair.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.065 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".