PWE-124 Daily aspirin 300mg: how much do patients take? drug accountability for the first 5 years of the aspect study
Bibliographic record
Abstract
Introduction We prescribe medicines to patients but the key factor determining efficacy is the dose taken. Recent evidence suggests compliance with aspirin therapy is vital to ensuring its chemoprevention efficacy. Therefore we look at the patient’s compliance with aspirin in a large trial. Method In the 2 × 2 factorial AspECT study, Barrett’s patients were randomised to take 300 mg Aspirin daily or no aspirin and to take 20 mg or 80 mg of PPI. This paper describes for the first 5 years after randomisation the aspirin taking pattern of those prescribed aspirin. Logistic regression analysis was used to consider what factors affected compliance. Results Of the 1116 patients randomised to aspirin, 803 (72%; 95% CI 69.2, 74.5) took aspirin for 5 years or until discontinuation related to the trial. Of the 803, 514 took aspirin at a dose of 300 mg and 77 took a lower dose for 5 years without interruption (150 mg (38), 75 mg (24), 150 mg later reducing to 75 mg (15)). The remaining 212/803 had interruptions as permitted in the protocol or discontinued due to trial related reasons (e.g. trial endpoints or protocol deviations). There was no evidence to suggest that gender, dose of PPI, age, ethnicity or length of Barrett’s predicted 5 year compliance. Conclusion Conclusion: In AspECT, 72% of patients were compliant with aspirin for 5 years or until discontinuation related to the trial. We did not discover any features predicting compliance. However since all patients were randomised to PPI, it does indicate longer term aspirin chemoprevention may be limited in over a quarter of the reflux population. Our thanks to the patients and the sites. The trial is sponsored by the University of Oxford with grant funding from CR-UK (A4584) and support from the Investigator-Sponsored Study Program of AstraZeneca Trial management and statistics provided by OCTO (OCTRU) and CSM Disclosure of Interest None Declared
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".