Factors Affecting Blood Pressure Variability: Lessons Learned from Two Systematic Reviews of Randomized Controlled Trials
Bibliographic record
Abstract
Systematic reviews can often reveal much more than the original objective of the work. The objectives of this retrospective analysis were to answer three basic questions about blood pressure variability: 1) Does blood pressure entry criterion have an effect on baseline blood pressure variability? 2) Do thiazide diuretics have a significant effect on blood pressure variability? and 3) Does systolic blood pressure vary to the same degree as diastolic blood pressure? This analysis of blood pressure variability is based on resting standardized research setting BP readings from two systematic reviews evaluating blood pressure lowering efficacy of thiazide diuretics from double blind randomized controlled trials in 33,611 patients with primary hypertension. The standard deviation reported in trials was the focus of the research and the unit of analysis. When a threshold systolic or diastolic blood pressure value is used to determine entry into a trial, baseline variability is significantly decreased, systolic from 14.0 to 9.3 mmHg and diastolic from 8.4 to 5.3 mmHg. Thiazides do not change BP variability as the standard deviation and coefficient of variation of systolic blood pressure and diastolic blood pressure did not differ between thiazide and placebo groups at end of treatment. The coefficient of variation of systolic blood pressure was significantly greater than the coefficient of variation of diastolic blood pressure. Entry criterion decreases the baseline blood pressure variability. Treatment with a thiazide diuretic does not affect blood pressure variability. Systolic blood pressure varies to a greater degree than diastolic blood pressure.
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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 | 0.431 | 0.767 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".