INTERVENTIONS TO IMPROVE CONTROL OF HYPERTENSION; WHAT WORKS (AND WHAT DOESN’T)?
Bibliographic record
Abstract
Objective: In undertaking a Cochrane systematic review of allied health professional-led interventions for hypertension, we found greater blood pressure (BP) lowering and BP target achievement with both nurse-led and pharmacist-led care, in comparison to usual (doctor-led) care. We identified elements of these complex interventions associated with successful outcomes. Here we present further analyses to describe key components of effective BP lowering interventions. Design and method: Univariable and multivariable meta-regression: study level descriptive variables for populations (setting, ethnicity, co-morbidity) and interventions (duration, review frequency and method, medication management, and BP target) were tested against change in systolic BP and achievement of study BP targets. Where univariable analyses suggested an association (P < 0.1) variables were included in multivariable models. The final model informed a hierarchical classification of interventions to compare effectiveness between groups. Scientific Data: Searches to July 2017 identified 1618 unique citations; 398 full texts were reviewed and 120 randomised controlled trials contributed data to the meta-regression dataset. Results: Reduction in systolic BP was negatively associated with telephone delivery of interventions (P = 0.02). A multivariable model including three factors: face to face delivery of care, frequency of intervention, and ability to change medication, accounted for one third of variance between studies (R2 = 34%). Hierarchical classification of studies based on these three elements predicted increasing magnitude of BP reduction (P < 0.001). Achievement of study BP targets was associated with both duration of interventions and the systolic BP target set (R2 = 14%). Relative risk for achievement of study targets declined as systolic BP targets were lowered, with evidence for effectiveness down to a target of 130 mmHg. Conclusions: Effective interventions to lower blood pressure require face to face contact. Telephone support or substitution appears ineffective. Ability to change medication is a key component of successful interventions, and review should take place at least monthly until BP is at target. Interventions are shown to be effective for systolic targets of 130 mmHg or higher, and are sensitive to duration of the intervention. These findings should inform future studies and guidelines.
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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 | 0.040 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.013 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".