Characterization and influencing factors of visit-to-visit blood pressure variability of the population in a northern Chinese industrial city
Bibliographic record
Abstract
BACKGROUND: Blood pressure variability (BPV) is a reliable prognostic factor for cardiovascular events. Currently there is a worldwide lack of large sample size studies in visit-to-visit BPV. Based on the Kailuan Study, we analyzed the visit-to-visit BPV of patients to investigate the range and influencing factors of BPV. METHODS: In 11 hospitals in the Kailuan Company, 4 441 patients received routine health checkups. Physical examination measured blood pressure (BP), body height, body weight, and waist circumference, and body mass index was calculated. Blood samples were analyzed for plasma total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose (FBG), and high-sensitivity c-reactive protein (hs-CRP). RESULTS: The effect of gender on systolic BPV was investigated. The average systolic BPV was 10.35 mmHg (1 mmHg = 0.133 kPa) overall, 10.54 mmHg in males and 10.06 mmHg in females. Multivariate Logistic regression analysis revealed that the age (RR = 1.022), systolic BP (SBP, RR = 1.007), LDL-C (RR = 1.098), and history of hypertension (RR = 1.273) were significant risk factors for higher systolic BPV. We found that aging (RR = 1.022), increased SBP (RR = 1.007), and a history of hypertension (RR = 1.394) were determinants of systolic BPV in males. The risk factors for systolic BPV of females were aging (RR = 1.017), increased SBP (RR = 1.009), increased LDL (RR = 1.136), and increased TG (RR = 1.157). CONCLUSION: Our findings indicated that the systolic BPV is closely associated with age, SBP and history of hypertension.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".