Prediagnostic use of low‐dose aspirin and risk of incident metastasis and all‐cause mortality among patients with colorectal cancer
Bibliographic record
Abstract
AIMS: Previous studies suggest that the use of low-dose aspirin before a colorectal cancer (CRC) diagnosis may be associated with a decreased risk of CRC progression. Data supporting this association, however, have been inconsistent. We evaluate whether the use of prediagnostic low-dose aspirin is associated with a lower risk of metastases and all-cause mortality in CRC patients. METHODS: Using a large Italian population-based primary care database, we identified a cohort of 7478 patients newly diagnosed with nonmetastatic CRC between 2000 and 2013. Use of prediagnostic low-dose aspirin was compared with no use of low-dose aspirin. Cox proportional hazards models were used to estimate adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) of incident metastasis and of all-cause mortality associated with prediagnostic low-dose aspirin use, both overall and by duration of use. RESULTS: There were 314 incident metastatic events and 2189 deaths during a mean follow-up time of 4.4 and 4.7 years, respectively. Overall prediagnostic use of low-dose aspirin was not associated with a decreased risk of incident metastasis (HR 0.88; 95% CI 0.63-1.22) or all-cause mortality (HR 1.09; 95% CI 0.96-1.22) in CRC patients. Cumulative duration of aspirin use was not associated with a decreased risk of incident metastasis (P-trend = .22) or all-cause mortality (P-trend = .38). These findings remained consistent in sensitivity analyses. CONCLUSION: In this real-world, population-based study, the prediagnostic use of low-dose aspirin was not associated with a decreased risk of incident metastasis or all-cause mortality in CRC patients.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".