Nighttime dipping status and risk of cardiovascular events in patients with untreated hypertension: A systematic review and meta‐analysis
Bibliographic record
Abstract
The objective of this systematic review and meta-analysis is to determine whether nocturnal blood pressure fall, expressed by dipping patterns according to ambulatory blood pressure monitoring (ABPM), is a risk factor for cardiovascular events (CVEs) in untreated hypertensives. Α thorough systematic literature search at MEDLINE, Embase, Cochrane Library, and gray literature was conducted through March 2020. Two reviewers screened studies and assessed dipping patterns of untreated hypertensives using ABPM with a follow-up >6 months. Newcastle-Ottawa scale was used for risk of bias assessment. We initially identified 463 reports; of which, seven cohort studies were eligible for meta-analysis enrolling 10 438 untreated hypertensives. Untreated patients classified as dippers at baseline (n = 7081) had significant lower risk of CVEs and total mortality compared to non-dippers (n = 3,357) [RR = 0.67, 95% CI (0.49, 0.92); RR = 0.71, 95% CI (0.59, 0.86)]. However, when patients were further classified into four dipping groups, only reverse dippers, yet not extreme dippers or non-dippers, were at increased risk for CVEs compared to dippers [RR = 0.47, 95% CI (0.33, 0.66)]. Likewise, only reverse dippers had a higher stroke risk than dippers [RR = 0.39, 95% CI (0.22, 0.72)]. When compared with the whole group of dippers (including extreme dippers), non-dipping alone (excluding reverse dipping) was not a significant risk factor for CVEs [RR = 0.84, 95% CI (0.61, 1.16)] or total mortality [RR = 0.84, 95% CI (0.61, 1.16); RR = 0.78, 95% CI (0.53, 1.13), respectively]. Untreated hypertensives may benefit more from the evaluation of reverse dipping rather than the non-dipping phenomenon in general.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".