Tolerability of losartan, angiotensin converting enzyme inhibitors and calcium channel blockers in current clinical practice
Bibliographic record
Abstract
The occurrence of side effects may contribute to a weak adherence to drug treatment. Therefore, losartan seems an attractive antihypertensive drug since, in clinical trials, the proportion of patients reporting side effects is lower among those on this drug than among those on angiotensin converting enzyme inhibitors (ACEI) or on calcium channel blockers (CCB). However, outside the experimental environment of clinical trials, little is known about the comparative tolerability of losartan with ACEI and CCB. We estimated the 3-month cumulative incidence of side effects among losartan, ACEI and CCB users, and assessed whether losartan is less associated with side effects than ACEI and CCB. We conducted a prospective cohort study through a network of 173 pharmacies across Canada where were identified individuals newly prescribed losartan, ACEI or CCB as a monotherapy. We interviewed individuals by telephone three times over a 3-month period. We analysed data using a multivariate logistic regression model. Out of 663 eligible individuals, the 3-month cumulative incidence of side effects was 52.5%, 60.2% and 69.6% for individuals treated with losartan, ACEI and CCB respectively. After adjustment for age, gender, level of education, symptoms perceived the week before entering the study, prior use of antihypertensive drugs, current use of any other drug, drug insurance coverage and duration of hypertension, the odds of reporting a side effect were significantly higher among individuals on ACEI (Odds ratios (OR) = 1.78, 95% Confidence Interval (CI) 1.02–3.12) or on CCB (OR = 2.65, 95% CI 1.47–4.78), than among those on losartan. In a naturalistic setting, we observed that losartan has a better tolerability than ACEI and CCB.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".