Aspirin Use, Compliance, and Knowledge of Anticancer Effect in the Community
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Millions of adults worldwide use low-dose aspirin for secondary prevention of heart disease. Results of randomized trials indicate that regular use of low-dose aspirin may reduce the risk of colorectal cancer by more than 20%, leading to speculation of its chemoprevention role for high-risk groups. Little is known, however, about the use of aspirin in our community. OBJECTIVE: To determine aspirin use and therapy compliance (never or rarely missing a dose) and to assess whether patients in our community are aware of its anticancer effect. METHODS: Observational study. Prospective data were collected during a 1-year period from patients in our general surgical clinic regarding aspirin use, comorbidities, adverse effects, and awareness of anticancer effect. Statistical analysis was performed. RESULTS: Among aspirin users (n = 137), the mean age was 65.8 years. Most (76.6%) received an 81-mg daily dose of aspirin. Compliance was 25.6% and was significantly associated with diabetes mellitus (p = 0.0028). Only 9.5% were aware of the medication's anticancer effect. Among nonusers (n = 383), the mean age was 53.3 years, a significant difference vs that of aspirin users (p < 0.001). Only 4.7% of nonusers knew of the anticancer effect. Nonusers were more likely to be women (p = 0.0005), younger than age 40 years (p < 0.0001), and have comorbidities or polypharmacy (p = 0.002). No significant difference was found between groups in anticoagulants use, nonsteroidal anti-inflammatory drug use, and smoking. CONCLUSION: Knowledge of aspirin's anticancer effect is low. More research is required to understand why aspirin compliance is also low.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it