Cause of Death Following Surgery for Acute Type A Dissection
Bibliographic record
Abstract
BACKGROUND: Surgery confers the best chance of survival following acute Type A dissection (ATAD), yet perioperative mortality remains high. Although perioperative risk factors for mortality have been described, information on the actual causes of death is sparse. In this study, we aimed to characterize the inciting events causing death during surgical repair of ATAD. METHODS: Nine centers participated in the study. We included all patients who died following surgical repair for ATAD between January 2007 and December 2013. An aortic surgeon at each site determined the primary cause of death from seven predetermined categories: cardiac, stroke, hemorrhage, other organ ischemia (peripheral, renal, or visceral), multiorgan failure, sepsis, or other causes. Additional characteristics and variables were analyzed to delineate potential modifiable factors for mortality. RESULTS: Of the 692 surgeries for ATAD, there were 123 deaths (17.8% mortality rate). Mean age at death was 66 years. Events contributing to death were: cardiac (25%), stroke (22%), hemorrhage (21%), multiorgan failure (12%), other organ ischemia (11%), sepsis (4%), and other causes (5%). Neurologic injury at presentation was a predictor of stroke as the inciting cause of death (p = 0.04). Peripheral, renal, or visceral ischemia at presentation was highly predictive of death due to these presenting ischemic conditions (p = 0.004). We found no associations between cardiogenic shock, tamponade, or cardiopulmonary bypass duration and cardiac death. CONCLUSION: Operative mortality for ATAD remains high in Canada. Nearly 70% of deaths arise from cardiac failure, stroke, or hemorrhage. Therefore, novel surgical, hybrid, and endovascular strategies should target these three areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".