Long-Term Survival in Patients Presenting With Type B Acute Aortic Dissection
Bibliographic record
Abstract
BACKGROUND: Follow-up survival studies in patients with acute type B aortic dissection have been restricted to a small number of patients in single centers. We used data from a contemporary registry of acute type B aortic dissection to better understand factors associated with adverse long-term survival. METHODS AND RESULTS: We examined 242 consecutive patients discharged alive with acute type B aortic dissection enrolled in the International Registry of Acute Aortic Dissection (IRAD) between 1996 and 2003. Kaplan-Meier survival curves were constructed, and Cox proportional hazards analysis was performed to identify independent predictors of follow-up mortality. Three-year survival for patients treated medically, surgically, or with endovascular therapy was 77.6+/-6.6%, 82.8+/-18.9%, and 76.2+/-25.2%, respectively (median follow-up 2.3 years, log-rank P=0.61). Independent predictors of follow-up mortality included female gender (hazard ratio [HR],1.99; 95% confidence interval [CI], 1.07 to 3.71; P=0.03), a history of prior aortic aneurysm (HR, 2.17; 95% CI, 1.03 to 4.59; P=0.04), a history of atherosclerosis (HR, 2.48; 95% CI, 1.32 to 4.66; P<0.01), in-hospital renal failure (HR, 2.55; 95% CI, 1.15 to 5.63; P=0.02), pleural effusion on chest radiograph (HR, 2.56; 95% CI, 1.18 to 5.58; P=0.02), and in-hospital hypotension/shock (HR, 12.5; 95% CI, 3.24 to 48.21; P<0.01). CONCLUSIONS: Contemporary follow-up mortality in patients who survive to hospital discharge with acute type B aortic dissection is high, approaching 1 in every 4 patients at 3 years. Current treatment and follow-up surveillance require further study to better understand and optimize care for patients with this complex disease.
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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".