Self-Measured Blood Pressure–Guided Pharmacotherapy: A Systematic Review and Meta-Analysis of United States-Based Telemedicine Trials
Bibliographic record
Abstract
BACKGROUND: The optimal approach to implementing telemedicine hypertension management in the United States is unknown. METHODS: We examined telemedicine hypertension management versus the effect of usual clinic-based care on blood pressure (BP) and patient/clinician-related heterogeneity in a systematic review/meta-analysis. We searched United States-based randomized trials from Medline, Embase, CENTRAL, CINAHL, PsycINFO, Compendex, Web of Science Core Collection, Scopus, and 2 trial registries. We used trial-level differences in BP and its control rate at ≥6 months using random-effects models. We examined heterogeneity in univariable metaregression and in prespecified subgroups (clinicians leading pharmacotherapy [physician/nonphysician], self-management support [pharmacist/nurse], White versus non-White patient predominant trials [>50% patients/trial], diabetes predominant trials [≥25% patients/trial], and White patient predominant but not diabetes predominant trials versus both non-White and diabetes patient predominant trials]. RESULTS: Thirteen, 11, and 7 trials were eligible for systolic and diastolic BP difference and BP control, respectively. Differences in systolic and diastolic BP and BP control rate were -7.3 mm Hg (95% CI, -9.4 to -5.2), -2.7 mm Hg (-4.0 to -1.5), and 10.1% (0.4%-19.9%), respectively, favoring telemedicine. Greater BP reduction occurred in trials where nonphysicians led pharmacotherapy, pharmacists provided self-management support, White patient predominant trials, and White patient predominant but not diabetes predominant trials, with no difference by diabetes predominant trials. CONCLUSIONS: Telemedicine hypertension management is more effective than clinic-based care in the United States, particularly when nonphysicians lead pharmacotherapy and pharmacists provide self-management support. Non-White patient predominant trials achieved less BP reduction. Equity-conscious, locally informed adaptation of telemedicine interventions is needed before wider implementation.
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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 | 0.023 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".