Abstract 601: Total Plaque Area Measurement Is A Reliable Indicator Of Vascular Risk Factor Treatment Effect
Bibliographic record
Abstract
Background: Ultrasound measurement of carotid plaque area (TPA) is a noninvasive method for identifying patients at higher risk of cardiovascular events. Previous studies demonstrated that intensive medical therapy reduces TPA. In this study we examined effects of less intensive therapy on TPA, and the usefulness of TPA in risk stratification. Methods. Baseline Framingham Risk Score (FRS), TPA, blood pressure (BP), body mass index and the post test probability using TPA (PtP TPA) were determined from patients at the primary care unit. TPA measurements were reported to primary physicians to encourage them to follow preventive guidelines. After 10±1 months patients were reevaluated to investigate changes from baseline. Results: among 140 patients enrolled between July 2009, and July 2011, 52% were male, BP was 142±1/81±1 mmHg, BMI 29±1 kg/m2, FRS 24±1, PtP TPA 50±2, and TPA 97±7 mm2. After 10±1 months of treatment, three groups were identified. Group 1 had a reduction of TPA (127±13 to 99±12 mm2, p<0.002), BP (142±3/80±2 to 137±2/75±2mmHg, p<0.002) and PtP TPA score (55±3 to 47±3, p<0.007). Group 2 patients had progression of TPA (from 90±8 to 138±12mm2, p<0.001) and reached a poorer PtP TPA score (51±2 to 58±2, p<0.001). Group 3 patients with stable TPA only decreased the BMI (from 28.5±0.9 to 27.8±1.0 kg/m2, p<0.05). TPA identified high-risk patients; 27% of these patients achieved regression of TPA, associated with a decrease of BP and PtP TPA. The rest of the patients only decreased the BMI, and the TPA remained stable. FRS did not change in any group in spite of changes on blood pressure or body weight. These data suggest that reduction in BP as well as reduction of BMI is required to prevent TPA progression. Conclusions: Plaque measurement may be useful for targeting preventive therapy and evaluating new treatments and response to therapy, and may improve cost-effectiveness of secondary preventive treatment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.007 | 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".