MASKED HYPERTENSION INCIDENCE
Bibliographic record
Abstract
Objective: Masked hypertension affects approximately 10 to 20% of the general population and is associated with a higher risk of cardiovascular diseases. No previous prospective study has examined risk factors associated with masked hypertension incidence. The aim of this study was to examine risk factors associated with masked hypertension incidence in a prospective cohort from Quebec City, Canada. Design and method: This is a dynamic cohort study using two pooled longitudinal samples of initially normotensive participants (Year 0 – Year 3; Year 3 – Year 5). The study sample was composed of 1,836 participants. At each time, blood pressure (BP) was measured using Spacelabs 90207. Manual BP was defined as the mean of the first three readings taken at rest. Ambulatory BP was defined as the mean of the next readings recorded every 15 minutes during daytime working hours. Risk factors of masked hypertension incidence were examined using cross-lagged generalized estimating equations. Results: After mutual adjustment, masked hypertension incidence was associated with male gender (RR = 1.53, 95% CI: 1.19–1.96), age (RR40–49 = 1.55, 95% CI: 1.14–2.10; RR > = 50 = 1.48, 95% CI: 1.05–2.09), body mass index (RR> = 27 = 1.45, 95% CI: 1.12–1.87), smoking status (RR = 1.49, 95% CI: 1.07–2.08) and alcohol intake (RR> = 6/week = 1.46, 95% CI: 1.06–2.00). Conclusions: Findings point toward sociodemographic and lifestyle related risk factors associated with masked hypertension incidence. These factors should be considered in screening efforts of individuals at risk for developing masked hypertension.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".