Aspirin use in prostate cancer and the risk of death and metastasis.
Bibliographic record
Abstract
1518 Background: There has been recent interest in the role of aspirin in preventing prostate cancer outcomes, but data remain limited. The objective was to determine whether the use of aspirin after prostate cancer diagnosis is associated with a decreased risk of prostate cancer mortality, distant metastasis and all-cause mortality in men newly-diagnosed with prostate cancer. Methods: A population-based cohort of men diagnosed with non-metastatic prostate cancer between 01/04/1998 and 31/12/2009 was identified using the UK Clinical Practice Research Datalink, including the Cancer Registry. All men were followed until death, distant metastasis, or 01/10/2012. A nested case-control analysis was performed where, for each case with an incident outcome event, up to 10 controls were matched on age, year of diagnosis and duration of follow-up. Exposure was defined as aspirin use during the matched follow-up period. Rate ratios (RR) and 95% confidence intervals (CI) were estimated using conditional logistic regression, adjusted for covariates and considering effect modification by aspirin use prior to prostate cancer diagnosis. Results: The cohort included 13,396 prostate cancer patients, followed for 3.9 (SD=2.4) years during which 4,425 deaths occurred, including 2,315 from prostate cancer, and 2,344 cases of distant metastasis. Aspirin use was associated with an increased risk of prostate cancer mortality (RR 1.36, 95% CI 1.18-1.55) and all-cause mortality (RR 1.33, 95% CI 1.21-1.47), but not distant metastasis (RR 1.09, 95% CI 0.94-1.27). The increased risks were limited to patients who did not use aspirin before diagnosis, for both prostate cancer mortality (RR 1.69, 95% CI 1.43-2.00) and all-cause mortality (RR 1.62, 95% CI 1.44-1.82), while those who used aspirin before diagnosis did not have increased risks (RR 0.93, 95% CI 0.76-1.15 and RR 0.98, 95% CI 0.85-1.13, respectively). Conclusions: The use of aspirin after prostate cancer diagnosis is not associated with a decreased risk of prostate cancer outcomes. Although increased risks were observed for all-cause and prostate cancer mortality, these effects were exclusively driven by new-users of aspirin, suggesting that aspirin use in these patients was likely related to disease progression.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".