Bibliographic record
Abstract
There is a lot of debate about the selection of the most appropriate treatments for hypertension. Most treatment recommendations are based on criteria that consider efficacy, safety and cost. Given the need for long-term utilization of these agents, treatment compliance should also be taken into consideration in the selection process. The purpose of this study was to estimate persistence and adherence to antihypertensive agents in a real life setting. This study was performed using data from the Regie de l'assurance maladie du Quebec (RAMQ). Persistence and adherence to treatment were estimated separately and an index combining these two measures was calculated. Persistence to treatment was calculated as the proportion of patients who had not definitively abandoned their treatment two years after they started it. Adherence to treatment was calculated as the proportion of patients for whom the ratio of the quantity of medication received over the quantity needed for the treatment period was above 80%. The persistence-adherence index was calculated by multiplying the monthly persistence rates by the monthly adherence rates over a two-year period. Data from a random sample of 64,175 subjects covered by the RAMQ drug plan and one of the antihypertensive agent reimbursed by the drug plan for the first time between January 1999 and December 2000 were analysed. After a two-year period, persistence to treatment varied across antihypertensive agents. Persistence rates to β-blockers, amlodipine, angiotensin II receptor antagonists, other calcium channel blockers, ACE inhibitors, other diuretics, hydrochlorothiazide, and chlorthalidone were 71%, 67%, 66%, 64%, 63%, 60%, 52% and 23% respectively. The persistence-adherence index at two year was 66% for ARAs, 63% for amlodipine, 62% for β-blockers, 61% for other CCBs, 60% for ACE inhibitors, 51% for hydrochorothiazide, 50 % for other diuretics and 32% for chlorthalidone. Persistence and adherence to treatment are essential to treatment success and varied substantially between the different therapeutic options. Results of this study indicate that, in a real life setting, patients are significantly less compliant to diuretics than to any other antihypertensive agents. Am J Hypertens (2004) 17, 114A–115A; doi: 10.1016/j.amjhyper.2004.03.299
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".