Bibliographic record
Abstract
The primary objectives of antihypertensive therapy are to achieve rapid blood pressure control and mitigate cardiovascular (CV) risk. To be effective in reducing risk, drugs must be taken regularly and for long periods of a patient's lifetime, despite any immediate perceived benefit for the patient. Thus, poor compliance to medication is a common problem among patients with hypertension, with side-effects often cited as a reason for discontinuing medication. More than 40% of patients discontinue treatment within 1 year, and it is widely considered that this is one of the main reasons why blood pressure control in clinical practice remains low.1,2 Safety and tolerability are key factors driving compliance and treatment adherence. Importantly, patients treated initially with a poorly tolerated medication are highly likely to discontinue from any follow-up treatment. Beside their efficacy profile, safety and tolerability are important factors for differentiation between drug classes. For example, recent meta-analyses have shown that some angiotensin-converting enzyme (ACE) inhibitors and angiotensin II receptors blockers (ARBs) may be similarly effective in reducing blood pressure and improving outcomes in hypertensive patients. However, ARBs are generally better tolerated than ACE inhibitors, with placebo-like tolerability, and a much lower incidence of the dry, persistent cough and angio-oedema associated with ACE inhibitors. In a population-based study of newly diagnosed hypertensives in Italy, the rate of treatment discontinuation was lowest with ARBs when compared with any other class of antihypertensive agent.1 In a separate analysis, higher levels of persistence with antihypertensive treatment at 1 year were seen with ARBs (18.8%) than with ACE inhibitors (11.4%) or any other type of medications.4 Among the ARBs, telmisartan monotherapy was associated with one of the highest levels of persistence with therapy.4 Furthermore, The ONTARGET® Trial Programme has demonstrated that telmisartan reduces the risk of CV death, myocardial infarction and stroke in a broad range of highrisk patients with a history of CV disease or with diabetes and end-organ damage and had a better safety profile and higher treatment adherence when compared with the ACE inhibitor ramipril.5 Key to improving rates of hypertension control and ensuring optimal management of CV risk is the selection of efficacious, well-tolerated drugs as first-line agents, thereby optimizing patient treatment adherence and improving clinical outcomes. Further reading Corrao G, Zambon A, Parodi A, et al. Discontinuation of and changes in drug therapy for hypertension among newly-treated patients: a population-based study in Italy J Hypertens. 2008;26:819–824. Vrijens B, Vincze G, Kristanto P, et al. Adherence to prescribed antihypertensive drug treatments: longitudinal study of electronically compiled dosing histories. BMJ. 2008;336:1114–1117. Conlin P, Gerth MBA, Fox J, et al. Four year persistence patterns among patients initiating therapy with the angiotensin II receptor antagonist losartan versus other antihypertensive drug classes Clin Ther. 2001:23:1999–2010. Costa FV, Esposti LD, Cerra C, Veronesi C, Buda S. Trends in prescription and determinants of persistence to antihypertensive therapy. High Blood Press Cardiovasc Prev. 2009;16:1–10. ONTARGET® Study Investigators. Telmisartan, ramipril, or both in patients at high risk for vascular events. N Engl J Med. 2008;358:1547–1559.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.007 |
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".