WHITE COAT HYPERTENSION AND ARTERIAL STIFFNESS: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Objective: Arterial damage of large arteries, addressed as carotid-femoral pulse wave velocity (c-f PWV), is recognized as independent predictor for future cardiovascular disease. The aim of this study was to investigate the impact of white coat hypertension on c-f PWV, in an untreated population. Design and method: PubMed and Cochrane Library were searched systematically to identify studies comparing c-f PWV levels between normotensives, hypertensives and white-coat hypertensives (WCH). Meta-analysis was performed to compare the difference c-f PWV levels between these groups. New Castle Ottawa quality assessment scale for case-control studies was used to assess study quality. Results: WCH patients had significantly increased c-f PWV values compared to the normotensive groups (d = 0.87 95% CI: 0.52 to 1.21, P < 0.01). Moreover, the hypertensive population was found to have statistically significant increased values of c-f PWV compared to WCH population (d = -0.74, 95% CI: -1.43 to -0.05, P < 0.01). Finally, there was no statistically significant difference between masked hypertension and WCH population (d = 0.06, 95% CI: -1.04 to 1.15, P < 0.001). Conclusions: WCH population have statistically significant increased values of c-f PWV compared to the normotensive group. These results demonstrate that WCH is not clinically innocent, in the untreated population.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.023 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".