Evolving Surgical Techniques and Improving Outcomes for Aortic Arch Surgery in Canada
Bibliographic record
Abstract
Background To explore evolving surgical techniques and outcomes for aortic arch surgery. Methods A total of 2435 consecutive patients underwent aortic arch repair with hypothermic circulatory arrest between 2008 and 2018 in 12 institutions across Canada. Trends in patient characteristics, surgical techniques, and in-hospital outcomes, including major morbidity or mortality, were examined. Results From 2008 to 2018, the age of patients (62.3 ± 13.2 years) and the proportion of women (30.2%) undergoing arch surgery did not change significantly. Aortic diameters at operation decreased (2008: 58 ± 13 mm; 2018: 53 ± 11 mm; P < 0.01). Surgeons performed more valve-sparing root replacements (2008: 0%; 2018: 15%; P < 0.001) and fewer Bentall procedures (2008: 27%; 2018: 20%; P < 0.01). Total arch replacement rates were similar ( P = 0.18); however, elephant trunk (2008: 9.5%; 2018: 19%; P < 0.001) and frozen elephant trunk (2008: 3.1%; 2018: 15%; P < 0.001) repair rates have increased. Over time, higher nadir temperatures (2008: 18 [17-21]°C; 2018: 25 [23-28]°C; P < 0.001), and more frequent antegrade cerebral perfusion (2008: 61%; 2018: 83%; P < 0.001) were used. For elective cases, in-hospital mortality rates declined (2008: 6.8%; 2018: 1.2%; P = < 0.01), as did major morbidity or mortality (2008: 24%; 2018: 13%; P < 0.001) and transfusion rates (2008: 61%; 2018: 41%; P < 0.001), but stroke rates remained constant (2008: 6.8%; 2018: 5.3%; P = 0.12). Outcomes remained the same over time for urgent or emergent cases. Conclusions Outcomes have improved over the past decade in Canada for elective aortic arch surgery, in the context of operating on smaller aortas, and more frequent use of moderate hypothermia and antegrade cerebral perfusion. Further research is needed to improve stroke rates and outcomes in the emergency setting.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".