Impact of Interhospital Transfer on Mortality for Acute Aortic Dissections
Bibliographic record
Abstract
BACKGROUND: There is a time-sensitive increase to mortality for acute type A dissections, yet interhospital transfer has not shown to increase operative death. Operative death does not account for all deaths attributable to transfer. We sought to determine the impact of transfer on mortality for all patients transferred with acute aortic dissections. METHODS: A retrospective study of deidentified health data captured between April 2003 and March 2020 for Ontario, Canada (14.7 million population) was performed to identify all patients hospitalized with acute aortic dissections. Nontransfers and interhospital transfers were reviewed and characteristics associated with transfer assessed. Associations between transfer and death were estimated using modified Poisson regression. RESULTS: There were 6218 acute aortic dissections (type A, n = 2641; type B, n = 3577). For 148 hospitals, 11 had onsite cardiac surgery. There was a 2.02-fold (95% CI, 1.66-2.45) increased risk of mortality for type A dissection patients transferred to a cardiac surgery hospital for surgical consideration (32.6%), relative to those at a cardiac surgery hospital who had surgery (16.3%). For patients who transferred alive and had surgery, there was no mortality difference (18.1% vs 16.3%, P = .37). A nontransfer palliation strategy was more likely for female patients (P < .001), age >75 years (P < .001), rural residence (P < .008), and increased Charlson index (P < .001). Type B dissections showed no difference in mortality across noncardiac and tertiary hospitals (9.8% vs 10.2%, P = .70). CONCLUSIONS: Transfers for acute type A dissections were associated with increased mortality. Palliation instead of transfer for surgical consideration was more common for women.
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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 | 0.001 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".