Aliskiren in Patients Failing to Achieve Blood Pressure Targets With Angiotensin Converting Enzyme Inhibitors or Angiotensin Receptor Blockers
Bibliographic record
Abstract
BACKGROUND: To assess the efficacy of aliskiren in patients failing to reach blood pressure (BP) goals with angiotensin converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB). METHODS: A total of 107 patients who failed to reach BP goals on ACEI or ARB were switched to aliskiren. Changes in BP were determined during maximal ACEI, ARB, or aliskiren therapy. RESULTS: Mean reduction in sBP and dBP with ACEI was 8.5 ± 6.3 mmHg and 6.0 ± 4.7 mmHg, respectively. Mean reduction in sBP and dBP with ARB was 8.3 ± 6.7 mmHg and 5.0 ± 5.2 mmHg, respectively. Mean reduction in sBP and dBP with aliskiren 150 mg/d was 6.7 ± 5.4 mmHg and 5.4 ± 4.8 mmHg, respectively. Mean reduction in sBP and dBP with aliskiren 300 mg/d was 8.6 ± 6.3 mmHg and 6.0 ± 4.9 mmHg, respectively. BP reductions between ACEI, ARB, and aliskiren were not significantly different. CONCLUSIONS: Aliskiren is ineffective in patients failing ACEI or ARB therapy. Given the label changes restricting the use of aliskiren in combination with ACEI and ARB, excess cost compared to ACEI and ARB, and a paucity of outcome data, there is a limited role for aliskiren in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".