Pharmacokinetic and clinical evaluation of esomeprazole and ASA for the prevention of gastroduodenal ulcers in cardiovascular patients
Bibliographic record
Abstract
INTRODUCTION: Low-dose aspirin (ASA, 75 - 325 mg/day) is widely used for the primary and secondary prevention of cardiovascular (CV) diseases. However, the value of primary prevention ASA is uncertain as the reduction in occlusive events needs to be weighed against the significant increase in major bleedings. Prevention with antisecretory drugs has been proposed to reduce the incidence of ASA-induced gastrointestinal (GI) bleedings, but non-adherence to gastro-protection is of concern, as it significantly increases the risk of upper GI adverse events. Beside patients and physicians education, one approach to overcome non-adherence is the development of fixed-dose combination. AREA COVERED: This review explores the results of clinical studies on the influence of the combination esomeprazole (ESA) and ASA on pharmacokinetic (PK) parameters, and the role for such combination in prevention of CV events in patients at risk of gastric ulcers. EXPERT OPINION: Patients at risk of ASA-induced gastroduodenal ulcer might benefit from a fixed ASA and proton pump inhibitor (PPI) combination. PK and PD parameters suggest there is no significant interaction between these drugs. Nevertheless, attention must be paid on the appropriate use of such combination, that is, still balancing the risk:benefit ratio in a real-life setting, and any increase in the proportion of patients receiving ASA and PPI should be considered as a warning signal.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".