Fixed-Dose Combination Medications for Treating Hypertension: A Review of Effectiveness, Safety, and Challenges
Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize the recent evidence on the effectiveness and safety of antihypertensive fixed-dose combination (FDC) medications, and to describe the facilitators and barriers to implementing FDCs in US clinical practice. RECENT FINDINGS: Recent clinical practice guidelines include FDC use for treating high BP. Clinical trials in recent years support the use of antihypertensive FDCs including low-dose triple- and quadruple-therapy FDCs. Initiating a low-to-standard dose dual-therapy FDCs showed better BP control than initiating treatment with a standard-dose monotherapy, and triple-therapy FDCs produced better BP control rates than dual-therapy FDCs. Retrospective cohort studies showed that FDCs are associated with increased medication adherence, reduced clinical inertia, decreased time to BP control, and improved cardiovascular outcomes. We further discussed barriers and facilitators of wider implementation of antihypertensive FDCs in clinical practice. FDC treatment for hypertension is not commonly used despite historical and recent data which support the effectiveness, safety, and benefits of FDCs. Simplified and protocolized treatment algorithms, team-based care, shared decision-making principles are crucial to successful utilization and implementation of FDC in clinical practice.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".