Pharmacist care in hypertension management: systematic review of randomized controlled trials
Bibliographic record
Abstract
Abstract Background Hypertension management remains a major public health challenge in primary care. Recent hypertension guidelines recommend the involvement of pharmacists for team-based care management of hypertension. Our objective is to systematically review the evidence of the impact of pharmacist care alone, or in collaboration, on BP amongst hypertensive outpatients compared with usual care. One major focus is to assess the heterogeneity in the effects of these interventions to identify which ones work best in a given healthcare setting. Methods In collaboration with a medical librarian, a systematic literature search was conducted for any article published up to 22.10.2021 in MEDLINE, EMBASE, CENTRAL, CINAHL, Web of Science, and Trip databases. Randomized controlled trials assessing the effect of pharmacist interventions on BP among outpatients were included. The outcomes are the change in BP, BP at follow-up, or BP control. Results will be synthesized descriptively and, if appropriate, will be pooled across studies to perform meta-analysis. We published the study protocol in BMJ Open. Results A total of 1768 study records were identified by electronic database searching and loaded to the systematic review management software Covidence. After removal of duplicates, 1744 were independently screened based on title and abstract by two authors (VG, ST), and 242 full texts were evaluated. A total of 72 studies with 32641 patients are currently included for data extraction. These studies were published between 1973 and 2021 and conducted in different regions (North America: n = 34, Europe: n = 13, other: n = 25). The data extraction and analysis are ongoing. Results will be presented at the congress. Conclusions This systematic review provides updated evidence on the effect of pharmacist intervention on BP management. Heterogeneity in the effect of interventions will be carefully evaluated which will help the implementation of effective interventions in various healthcare settings. Key messages • Recent hypertension guidelines recommend the involvement of pharmacists for team-based care management of hypertension. • This systematic review provides updated evidence on the effect of pharmacist intervention on blood pressure management.
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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 | 0.028 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.015 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".