Team-Based Care for Improving Hypertension management: the TBC-HTA randomized controlled study
Bibliographic record
Abstract
Background Blood pressure (BP) is poorly controlled among a large proportion of hypertensive outpatients in European countries. Innovative and practical models of care are needed to improve BP control. Our objective is to determine whether a team-based care (TBC) interprofessional intervention, involving physicians, nurses, and pharmacists improves BP control compared to usual care among uncontrolled treated hypertensive outpatients. Methods & Results Using a pragmatic multicenter randomized controlled study, we conduct the Team-Based Care for Improving Hypertension (TBC-HTA) study in ambulatory clinics and nearby community pharmacies in Lausanne and Geneva, Switzerland (ClinicalTrials.gov registration: NCT0251109). Treated uncontrolled hypertensive patients are recruited from ambulatory clinics and randomized to receive either TBC intervention (TBC: N = 55) by specially trained nurses and pharmacists working in collaboration with physicians or usual care (UC: N = 55). Every 6-week, TBC patients receive nurse and pharmacist intervention (BP measurement, assessment and counselling about lifestyle and medication adherence, and health education concerning treatment and disease) respectively. Following each visit, a summary report (BP data, medication adherence, and lifestyle) and recommendations are prepared by nurse and pharmacist for the physician who adjusts antihypertensive therapy accordingly. The primary outcome is the difference in daytime ambulatory BP between TBC and UC patients at 6-month of follow-up. Secondary outcomes include patients’ and healthcare professionals’ satisfaction with the TBC intervention. Results will be presented at the congress. Conclusions This ongoing study aims to evaluate a new approach focused on TBC interventions engaging multiple healthcare professionals to control hypertension. This study will provide high-level evidence on the effect of a team-based care of hypertension in the Swiss primary care setting. Key messages: Innovative and practical models of care are needed to improve blood pressure control in European countries. This study will provide high-level evidence on the effect of a team-based care of hypertension in the Swiss primary care setting.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".