Effects of non‐selective non‐steroidal anti‐inflammatory drugs on the aggressiveness of prostate cancer
Bibliographic record
Abstract
BACKGROUND: Inflammatory mediators have a role in the initiation and progression of prostate cancer. Observed anti-cancer effects of non-steroidal anti-inflammatory drugs (NSAIDs) have consisted largely of those that inhibit inflammatory mechanisms thought to promote an aggressive disease phenotype. Epidemiologic studies have supported a chemopreventive effect but there is little research on a possible protective role against prostate cancer aggressiveness and progression to advanced disease. METHODS: We conducted a population-based exploratory study, using cross-sectional and case-cohort approaches to assess, the effect of NSAIDs on indicators of prostate cancer aggressiveness. The study population consisted of 1,619 randomly selected patients with a further over-sampling of 453 prostate cancer mortality cases. All had been curatively treated by radical prostatectomy or external-beam radiotherapy and were sampled using the Ontario Cancer Registry. Aggressiveness of disease at diagnosis, represented by Gleason score, and risk of prostate cancer death were compared across NSAID exposure groups. RESULTS: The adjusted odds ratio (OR) of a total Gleason score of 8-10 versus 2-6 indicated a non-significant protective effect of NSAIDs (OR: 0.74, 95% CI: 0.47-1.17). We did not observe an association with risk of prostate cancer death overall (HR: 1.03, 95% CI: 0.79-1.34), but a secondary analysis indicated that NSAID users surviving five years may be protected from early prostate cancer death (HR: 0.54, 95% CI: 0.26-1.13). CONCLUSION: Although estimates were not statistically significant, this exploratory study indicates a possible negative association between NSAID use and disease aggressiveness. Larger investigations with more precise exposure measurements are recommended.
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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 | 0.002 |
| 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 | 0.000 |
| 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".