Assessment of management of cardiovascular risk factors in patients with thoracic aortic disease
Bibliographic record
Abstract
BACKGROUND: Cardiovascular risk profiles and adiposity assessment data in patients with thoracic aortic disease (TAD) are sparse. HYPOTHESIS: Despite the fact that TAD patients are considered as a high-risk population, they will not be managed as aggressively as they should in terms of their cardiovascular risk profile. MATERIALS AND METHODS: Anthropometric, blood pressure (BP) data, and blood samples were collected prospectively from patients followed at our TAD dedicated clinic. The same measures have been taken in a control group initiating a cardiac rehabilitation program. RESULTS: In all, 286 patients with TAD and 116 controls were recruited. BMI was higher among the controls than the patients with TAD (30.0±6.1 vs. 27.2±4.9 kg/m(2), respectively; P<0.001). We found no statistical difference between the groups for waist circumference (TAD: 93.1±15.2 and 103.6±14.5 cm, control: 95.0±13.4 and 106.3±14.8 cm, respectively, for women and men; P=0.06). In terms of lipid profile, low-density lipoprotein cholesterol was 2.44±0.88 in patients with TAD and 2.09±0.82 mmol/l in the controls (P<0.001). A higher percentage of patients with TAD had low-density lipoprotein cholesterol values that were beyond the target (63.3% in TAD vs. 46.1% in control; P<0.01). The values of apolipoprotein-B were similar between groups (P=0.41). BP was higher in patients with TAD (135±19/76±11 mmHg) versus the controls (129±17/71±11 mmHg; P<0.01). On the basis of ambulatory BP monitoring, 49.3% of patients with TAD were not well controlled for daytime and/or night-time BP. CONCLUSION: Cardiovascular risk factors, particularly BP, are not well controlled in patients with TAD followed in a dedicated clinic when compared with another high-risk control group in a cardiac rehabilitation program.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".