Effectiveness of fixed-dose combination therapy in hypertension: systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Clinical studies have revealed that fixed-dose combinations (FDCs) of drugs can have a better effect on blood pressure than free-equivalent combinations (FECs). Our objectives were to perform an up-to-date assessment of the effectiveness of FDCs and FECs in antihypertensive therapy, to provide more accurate results by using a stratified meta-analysis. Material and methods: A systematic review was performed in PubMed, Web of Science, and Cochrane databases according to PRISMA guidelines. The outcomes were adherence (compliance), persistence to medication, reduction of blood pressure and the safety profile. We used the Newcastle Ottawa scale or the Delphi list for the assessment of the quality of cohort studies or clinical trials, respectively. Heterogeneity was assessed using the Cochrane Q test and I 2 statistic. Results: Of 301 abstracts screened, 26 primary studies and 2 other metaanalyses were identified, of which 12 studies were included in the metaanalyses and 3 studies were included in the narrative review. The FDC treatment is associated with a significant improvement in adherence and persistence in comparison with FEC treatment, e.g., the average medicine possession ratio increased with FDC by 13.1% (p < 0.001). For endpoints correlated with higher adherence (e.g., a reduction in blood pressure), a nonsignificant benefit was observed for FDCs. Moreover, it was demonstrated that higher adherence can lead to a lower risk of cardiovascular events. Conclusions: In comparison with FECs, the FDC treatment is associated with a significant improvement in the cooperation between a doctor and a patient and with increased patients' adherence to the treatment schedule.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.025 | 0.043 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".