Impact of Successive Office Blood Pressure Measurements During a Single Visit on Cardiovascular Risk Prediction: Analysis of CARTaGENE
Bibliographic record
Abstract
BACKGROUND: Multiple office blood pressure (BP) readings correlate more closely with ambulatory BP than single readings. Whether they are associated with long-term outcomes and improve cardiovascular risk prediction is unknown. Our objective was to assess the long-term impact of multiple office BP readings. METHODS: We used data from CARTaGENE, a population-based survey comprising individuals aged 40 to 70 years. Three BP readings (BP 1 , BP 2 , and BP 3 ) at 2-minute intervals were obtained using a semiautomated device. They were averaged to generate BP 1-2 , BP 2-3 , and BP 1-2-3 for systolic BP (SBP) and diastolic BP. Cardiovascular events (major adverse cardiovascular event [MACE]: cardiovascular death, stroke, and myocardial infarction) during a 10-year follow-up were recorded. Associations with MACE were obtained using adjusted Cox models. Predictive performance was assessed with 10-year atherosclerotic cardiovascular disease scores and their associated C statistics. RESULTS: In the 17 966 eligible individuals, 2378 experienced a MACE during follow-up. Crude SBP values ranged from 122.5 to 126.5 mm Hg. SBP 3 had the strongest association with MACE incidence (hazard ratio, 1.10 [1.05–1.15] per SD) and SBP 1 the weakest (hazard ratio, 1.06 [1.01–1.10]). All models including SBP 1 (SBP 1 , SBP 1-2 , and SBP 1-2-3 ) were underperformed. At a given SBP value, the excess MACE risk conferred by SBP 3 was 2× greater than SBP 1 . In atherosclerotic cardiovascular disease scores, SBP 3 yielded the highest C statistic, significantly higher than most other SBP measures. In contrast to SBP, all diastolic BP readings yielded similar results. CONCLUSIONS: Cardiovascular risk prediction is improved by successive office SBP values, especially when the first reading is discarded. These findings reinforce the necessity of using multiple office BP readings.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".