Adding Pharmacist-Led Home Blood Pressure Telemonitoring to Usual Care for Blood Pressure Control: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Health systems have been quickly adopting telemedicine throughout the United States, especially since the onset of the COVID-19 pandemic. However, there are limited data on whether adding pharmacist-led home blood pressure (BP) telemonitoring to office-based usual care improves BP. We searched PubMed/MEDLINE and Embase for randomized controlled trials from January 2000 until April 2022, comparing studies on pharmacist-led home BP telemonitoring with usual care. Six randomized controlled trials, including 1,550 participants, satisfied the inclusion criteria. There were 774 participants in the pharmacist-led telemonitoring group and 776 in the usual care group. The addition of pharmacist-led telemonitoring to usual care was associated with a significant decrease in systolic BP (mean difference −8.09, 95% confidence interval −11.15 to −5.04, p <0.001, I 2 = 72%) and diastolic BP (mean difference −4.19, 95% confidence interval −5.58 to −2.81, p <0.001, I 2 = 42%) compared with usual care. In conclusion, this meta-analysis showed that adding pharmacist-led home BP telemonitoring to usual care achieves better BP control than usual care alone.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".