Impact of brain protection strategies on mortality and stroke in patients undergoing aortic arch repair with hypothermic circulatory arrest: evidence from the Canadian Thoracic Aortic Collaborative
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to investigate the impact of various brain perfusion techniques and nadir temperature cooling strategies on outcomes after aortic arch repair in a contemporary, multicentre cohort. METHODS: A total of 2520 patients underwent aortic arch repair with hypothermic circulatory arrest (HCA) between 2002 and 2018 in 11 centres of the Canadian Thoracic Aortic Collaborative. Primary outcomes included mortality; stroke; a composite of mortality or stroke; and a Society of Thoracic Surgeons-defined composite (STS-COMP) end point for mortality or major morbidity including stroke, reoperation, renal failure, prolonged ventilation and deep sternal wound infection. Multivariable logistic regression and propensity score matching were performed for cerebral perfusion and nadir temperature practices. RESULTS: Antegrade cerebral perfusion was found on multivariable analysis to be protective against mortality [odds ratio (OR) 0.64, 95% confidence interval (CI) 0.48-0.86; P = 0.005], stroke (OR 0.55, 95% CI 0.37-0.81; P = 0.006), composite of mortality or stroke (OR 0.57, 95% CI 0.45-0.72; P = 0.0001) and STS-COMP (OR 0.53, 95% CI 0.41-0.67; P < 0.0001), as compared to HCA alone. Retrograde cerebral perfusion yielded similar outcomes as compared to antegrade cerebral perfusion. When compared to HCA with nadir temperature <24°C, a propensity score analysis of 647 matched pairs identified nadir temperature ≥24°C as predictor of lower mortality (OR 0.62, 95% CI 0.40-0.98; P = 0.04), stroke (OR 0.51, 95% CI 0.31-0.84; P = 0.008), composite of mortality or stroke (OR 0.62, 95% CI 0.43-0.89; P = 0.01) and STS-COMP (OR 0.64, 95% CI 0.49-0.85; P = 0.002). CONCLUSIONS: Antegrade cerebral perfusion and nadir temperature ≥24°C during HCA for aortic arch repair are predictors of improved survival and neurological outcomes.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".