IMPACT OF SEASONAL BLOOD PRESSURE CHANGES ON VISIT-TO-VISIT BLOOD PRESSURE VARIABILITY
Bibliographic record
Abstract
Objective: Visit-to-visit blood pressure (BP) variability (V) associates with an increased risk of cardiovascular events. Limited information is available, however, on the factors responsible for this phenomenon. We investigated the role of seasonal BP modifications on the magnitude of BPV and its impact on cardiovascular risk. Design and method: In 28365 patients included in the ONTARGET and TRANSCEND trials the on-treatment systolic (S) BP values were grouped according to the month in which they were obtained. SBP differences between winter and summer months were calculated for each BPV quintile (Q), quantified by the coefficient of variation (CV) of between-visits mean SBP. The differences in the risk of morbid and fatal cardiovascular events between Qs were assessed by the Cox regression model. Results: SBP was 4 mmHg lower in summer than in winter regardless of sex, age, diabetes, baseline SBP and achieved SBP of the patients. Winter/summer SBP differences contributed significantly to each SBP-CV and the contribution increased progressively from Q1 to 5. Increase of SBP-CV from Q1 to Q5 was associated with a progressive increase in the adjusted hazard ratio of the primary endpoint of the trials, i.e.morbid and fatal cardiovascular events (Q5 vs Q1: 1.51, 95% CI 1.36 - 1.67). A similar trend was observed for secondary endpoints. This was also the case after subtraction of the SBP seasonality. Conclusions: Winter/summer SBP differences significantly contribute to visit-to-visit SBP variability and more so as variability becomes greater. This contribution, however, does not explain the adverse prognostic significance of visit-to-visit BP variations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".