Increasing Prevalence of Hypertension Among Patients With Thoracic Aorta Dissection: Trends Over Eight Decades—A Structured Meta-analysis
Bibliographic record
Abstract
BACKGROUND: This structured metaanalysis focused on determining the relationship between hypertension (HTN) and thoracic aortic dissection (TAD). METHODS: Electronic searches were conducted using the MedLine database, for the period 1946 through May 2013, and manual searches from reference lists. Demographic data, patient diagnosis, and HTN prevalence were extracted from each study. Data were analyzed using weighted averages, metaanalysis, analysis of variance, trend analysis, and multivariate analysis. RESULTS: A total of 8,086 cases of TAD from 75 studies over eight decades were assessed. Overall prevalence of HTN in TAD was 66.7% ± 17.5%. An increase of approximately 5.6% in HTN prevalence in TAD cases occurred in every decade. Prevalence of HTN in type A dissections steadily increased, with an overall prevalence of 64.8% ± 21.3%, while in type B dissections, prevalence abruptly increased from 1950 to 1970 and remained constant thereafter, with an overall prevalence of 78.7% ± 8.6%. Trend analysis demonstrated significant (P < 0.001) and linear increasing trends for the prevalence of HTN and age at presentation. Multivariate analysis demonstrated that a history of HTN was significantly (P < 0.001) associated with increasing trends of over time, which was independent of the relationship between age and TAD. CONCLUSIONS: The proportion of TAD patients with HTN has been increasing over eight decades. Age at presentation of TAD has also been incrementally increasing, but the increase in HTN was independent of age in multivariate analysis. The trend for increasing HTN prevalence was more evident in type A TAD. These data highlight a need to focus on HTN management in patients with thoracic aortic aneurysm.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.036 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".