Carotid Plaque Area
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Carotid plaque area measured by ultrasound (cross-sectional area of longitudinal views of all plaques seen) was studied as a way of identifying patients at increased risk of stroke, myocardial infarction, and vascular death. METHODS: Patients from an atherosclerosis prevention clinic were followed up annually for up to 5 years (mean, 2.5+/-1.3 years) with baseline and follow-up measurements recorded. Plaque area progression (or regression) was defined as an increase (or decrease) of >/=0.05 cm(2) from baseline. RESULTS: Carotid plaque areas from 1686 patients were categorized into 4 quartile ranges: 0.00 to 0.11 cm(2) (n=422), 0.12 to 0.45 cm(2) (n=424), 0.46 to 1.18 cm(2) (n=421), and 1.19 to 6.73 cm(2) (n=419). The combined 5-year risk of stroke, myocardial infarction, and vascular death increased by quartile of plaque area: 5.6%, 10.7%, 13.9%, and 19.5%, respectively (P<0.001) after adjustment for all baseline patient characteristics. A total of 1085 patients had >/=1 annual carotid plaque area measurements: 685 (63.1%) had carotid plaque progression, 306 (28.2%) had plaque regression, and 176 (16.2%) had no change in carotid plaque area over the period of follow-up. The 5-year adjusted risk of combined outcome was 9.4%, 7.6%, and 15.7% for patients with carotid plaque area regression, no change, and progression, respectively (P=0.003). CONCLUSIONS: Carotid plaque area and progression of plaque identified high-risk patients. 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".