Normal Systolic Blood Pressure and Risk of Heart Failure in US Male Physicians
Bibliographic record
Abstract
AIMS: Heart failure (HF) is a major public health issue and hypertension is a major predictor of HF. Observational studies have demonstrated a continuous and graded relationship between 'normal' systolic blood pressure (SBP) and cardiovascular disease. However, limited data are available on the relationship between normotensive SBP and the risk of HF. METHODS AND RESULTS: To test the hypothesis that there is a graded relation between SBP and HF risk among subjects with normal SBP, we used data on 18 876 participants who were healthy and were free of HF at baseline. Incident HF cases were ascertained by annual follow-up questionnaires and validated through a review of medical records. Cox proportional hazard model was used to compute multivariable-adjusted hazard ratios with corresponding 95% confidence intervals. Between 1982 and 2008, 1098 cases of HF occurred. There was a 35% increased risk of HF among subjects with SBP 130-139 mmHg compared with people with optimal SBP (<120 mmHg). In addition, there was a linear trend in HF risk across the normal range of SBP. CONCLUSION: Our findings suggest a linear relationship between normotensive SBP and HF risk. Strategies to prevent HF, such as lifestyle modification, should be emphasized across all blood pressure ranges.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".