Chronobiological Patterns of Acute Aortic Dissection
Bibliographic record
Abstract
BACKGROUND: Chronobiological rhythms have been shown to influence the occurrence of a variety of cardiovascular disorders. However, the effects of the time of the day, the day of the week, or monthly/seasonal changes on acute aortic dissection (AAD) have not been well studied. METHODS AND RESULTS: Accordingly, we evaluated 957 patients enrolled in the International Registry of Acute Aortic Dissection (IRAD) between 1996 and 2000 (mean age 62+/-14 years, type A 61%). A chi2 test for goodness of fit and partial Fourier analysis were used to evaluate nonuniformity and rhythmicity of AAD during circadian, weekly, and monthly periods. A significantly higher frequency of AAD occurred from 6:00 AM to 12:00 noon compared with other time periods (12:00 noon to 6:00 PM, 6:00 PM to 12:00 midnight, and 12:00 midnight to 6:00 AM; P<0.001 by chi2 test). Fourier analysis showed a highly significant circadian variation (P<0.001) with a peak between 8:00 AM and 9:00 AM. Although no significant variation was found for the day of the week, the frequency of AAD was significantly higher during winter (P=0.008 versus other seasons by chi2 test). Fourier analysis confirmed this monthly variation with a peak in January (P<0.001). Subgroup analysis identified a significant association for all subgroups with circadian rhythmicity. However, seasonal/monthly variations were observed only among patients aged <70 years, those with type B AAD, and those without hypertension or diabetes. CONCLUSIONS: Similar to other cardiovascular conditions, AAD exhibits significant circadian and seasonal/monthly variations. Our findings may have important implications for the prevention of AAD by tailoring treatment strategies to ensure maximal benefits during the vulnerable periods.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".