Influence of Smoking, Body Mass Index, and Other Factors on the Preventive Effect of Nonsteroidal Anti-Inflammatory Drugs on Colorectal Cancer Risk
Bibliographic record
Abstract
Abstract Nonsteroidal anti-inflammatory drugs’ (NSAID) use has consistently been associated with lower risk of colorectal cancer; however, studies showed inconsistent results on which cohort of individuals may benefit most. We performed multivariable logistic regression analysis to systematically test for the interaction between regular use of NSAIDs and other lifestyle and dietary factors on colorectal cancer risk among 11,894 cases and 15,999 controls. Fixed-effects meta-analyses were used for stratified analyses across studies for each risk factor and to summarize the estimates from interactions. Regular use of any NSAID, aspirin, or nonaspirin NSAIDs was significantly associated with a lower risk of colorectal cancer within almost all subgroups. However, smoking status and BMI were found to modify the NSAID–colorectal cancer association. Aspirin use was associated with a 29% lower colorectal cancer risk among never-smokers [odds ratios (OR) = 0.71; 95% confidence intervals (CI): 0.64–0.79], compared with 19% and 17% lower colorectal cancer risk among smokers of pack-years below median (OR, 0.81; 95% CI, 0.71–0.92) and above median (OR, 0.83; 95% CI, 0.74–0.94), respectively (P interaction = 0.048). The association between any NSAID use and colorectal cancer risk was also attenuated with increasing BMI (P interaction = 0.075). Collectively, these results suggest that obese individuals and heavy smokers are unlikely to benefit as much as other groups from the prophylactic effect of aspirin against colorectal cancer. Significance: Obesity and heavy smoking attenuate the benefit of aspirin use for colorectal cancer prevention. Cancer Res; 78(16); 4790–9. ©2018 AACR.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".