Significance of Cardiac Rehabilitation on Visit-to-Visit Variability of Blood Pressure in Patients With Cardiovascular Disease in a 12-Month Follow-Up
Bibliographic record
Abstract
BACKGROUND: Visit-to-visit variability (VVV) in blood pressure (BP) has been shown to be a strong predictor of cardiovascular disease (CVD). However, the long-term effect of comprehensive cardiac rehabilitation (CR) with exercise training on VVV in BP has not yet been established. Therefore, we evaluated the long-term effects of CR on VVV in BP in patients with CVD. METHODS: Twenty-two CVD patients in a 12-month CR program who had at least six clinic visits per month to measure BP were enrolled. We determined VVV in BP expressed as the standard deviation of average BP every month for 12 months. RESULTS: . In addition, the percentage (%) of males, % heart failure and % ischemic heart disease were 77%, 55% and 27%, respectively. Patients who had uncontrolled BP at baseline showed a significant reduction of both systolic BP (SBP) and diastolic BP (DBP). VVV in SBP in the first month was significantly less than that in the last month, although there was no difference in VVV in DBP. Patients were divided into larger (L-) and smaller (S-) VVV in SBP groups according to the average value of VVV in SBP as a cut-off. The L-VVV in SBP group, but not the S-VVV in SBP group, showed a significant reduction of VVV in SBP. CONCLUSION: Comprehensive CR may improve VVV in SBP in CVD patients who have larger VVV in SBP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".