Predicting Blood Pressure and Heart Rate Change With Cardiovascular Reactivity and Recovery: Results From 3-Year and 10-Year Follow Up
Bibliographic record
Abstract
OBJECTIVES: We examined whether cardiovascular reactivity and recovery after laboratory-induced stress is useful in predicting 3-year and 10-year ambulatory blood pressure (BP) and heart rate (HR) among 330 initially normotensive individuals. METHODS: At baseline, BP and HR measurements were recorded during three 5-minute laboratory challenges and three 5-minute recovery periods after each challenge. Measurements of systolic BP, diastolic BP, and HR were collected throughout this baseline protocol and also at 3-year and 10-year follow up by ambulatory monitoring. RESULTS: After adjustment for traditional biologic predictors, reactivity was found to explain significant variance in follow-up data across all 3-year indices and two of the 10-year indices. Recovery, entered in a following step after reactivity, was found to explain additional significant variance across all 3-year indices but none of the 10-year indices. Family hypertension history data were not found to be significantly associated with reactivity or recovery nor were they predictive of longitudinal ambulatory data after adjustment for initial resting cardiovascular levels. CONCLUSION: Overall, from a hierarchical regression model perspective, the data support the use of both reactivity and recovery in clinical predictions of proximal BP and HR and generally support the use of reactivity (but not recovery) in long-term BP predictions.
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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 | 0.004 |
| 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".