Evidence for pharmacist care in the management of hypertension
Bibliographic record
Abstract
Hypertension is a major risk factor for cardiovascular diseases and mortality, affecting about 20% to 30% of North American adults.1,2 Although treatment of hypertension can substantially reduce this risk,3 hypertension in primary care remains underdetected, undertreated and poorly controlled.2,4,5 For example, despite substantial improvement, half of North American–treated patients with hypertension remain uncontrolled.2,4,5 Innovative models of care are therefore needed to improve patient outcomes, particularly in light of the heavy workload and shortage of family physicians in most health care systems. Some authors advocate a greater use of community-based models of care6 with the involvement of nonphysician clinicians, such as pharmacists and nurses, as a promising avenue to improve hypertension care and, more broadly, chronic disease management.7-9 Given their accessibility and drug therapy expertise, pharmacists are a logical choice and a valuable asset to improve hypertension management—alone or via team-based care.7,10-13 Indeed, there have been many trials of pharmacist care. We recently combined and updated 2 systematic reviews and meta-analyses of randomized controlled trials evaluating the effect of pharmacist interventions—alone or as part of collaborative care—on blood pressure outcomes among outpatients.12,14 Details about the type of pharmacist intervention (including description and frequency), the involvement of other health care professionals within collaborative care setting, the care setting and the characteristics of participants were also examined.14 The research methods (search strategy, study selection and data extraction) are given in detail in the original publication.14
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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 | 0.035 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.037 | 0.002 |
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".