The Clinical Impact of Imaging Surveillance and Clinic Visit Frequency after Acute Aortic Dissection
Bibliographic record
Abstract
Background Guidelines recommend frequent follow-up after acute aortic dissection (AAD), but optimal rates of follow-up are not clear. Methods We examined rates of imaging and clinic visits in 267 individuals surviving AAD during recommended intervals (≤1, > 1–3, > 3–6, > 6–12 months, then annually), frequency of adverse imaging findings, and the relationship between follow-up and mortality. Results Type A and B AAD were noted in 46 and 54% of patients, respectively. Mean follow-up was 54.7 ± 13.3 months, with 52 deaths. Adverse imaging findings peaked at 6 to 12 months (5.6%), but rarely resulted in an intervention (3.4% peak at 6–12 months). Compared with those with less frequent imaging, patients with imaging for 33 to 66% of intervals (p = 0.22) or ≥66% of intervals (p = 0.77) had similar adjusted survival. In comparison to patients with fewer clinic visits, those with visits in 33 to 66% of intervals experienced lower adjusted mortality (hazards ratio: 0.47, 95% confidence interval: 0.23–0.97, p = 0.04), with no difference seen in those with ≥66% (vs. < 33%) interval visits (p = 0.47). Imaging at 6 to 12 months (vs. none) was associated with decreased adjusted mortality (hazards ratio: 0.50, 95% confidence interval: 0.27–0.91, p = 0.02), while imaging during other intervals, or clinic visits during any specific intervals, was not associated with a difference in mortality (p > 0.05 for each). Conclusions Adverse imaging findings following AAD are common, but rarely require prompt intervention. Patients with the lowest and highest rates of clinic visits experienced increased mortality. While the overall rate of surveillance imaging did not correlate with mortality, adverse imaging findings and related interventions peaked at 6 to 12 months after AAD, and imaging during this time was associated with improved survival.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".