Current Place of Beta-Blockers in the Treatment of Hypertension
Bibliographic record
Abstract
Hypertension represents the most common cardiovascular risk factor, affecting more than 25% of the adult population in developed societies. Although beta-blockers have been previously shown to effectively reduce blood pressure and have been used for hypertension treatment for over 40 years, their effect on cardiovascular morbidity and mortality in hypertensive patients remains controversial and their use in uncomplicated hypertension is currently still under debate. According to the previous recommendations beta-blockers should not be preferred as first-line therapy in hypertension patients. This review summarizes the current knowledge on application of beta-blockers in patients with hypertension and discusses the most recent guidelines of the European Society of Hypertension (2009) on beta-blockers applications. Keywords: Hypertension, antihypertensive treatment, beta-blocker, cardiovascular risk, beta-blockers, blood pressure, carteolol, carvedilol, labetalol, nadolol, penbutolol, pindolol, propranolol, timolol, atenolol, betaxolol, bisoprolol, metoprolol, nebivolol, diuretics, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, calcium channel blockers, National Institute for Health and Clinical Excellence, ESH/ESC 2007, European Lacidipine Study on Atherosclerosis, Losar-tan Intervention For Endpoint, Anglo-Scandinavian Cardiac Outcomes Trial, Blood Pressure Low-ering Arm, International Verapamil-Trandolapril Study, Metoprolol Atherosclerosis Prevention in Hypertension, ACE inhibitors, Conduit Artery Function Evaluation, Canadian Hypertension Education Program, coronary artery disease CHF, congestive heart failure
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".