Effect of NSAIDs on the recurrence of nonmelanoma skin cancer
Bibliographic record
Abstract
Experimental studies have consistently shown a protective effect of nonsteroidal antiinflammatory drugs (NSAIDs) against nonmelanoma skin cancers (NMSC). However, little human epidemiological research has been done in this regard. We used data from the Skin Cancer Chemoprevention Study to explore the association of NSAID use and with the risk of basal-cell carcinoma (BCC) and squamous-cell carcinoma (SCC). 1,805 subjects with a recent history of NMSC were randomized to placebo or 50 mg of daily beta-carotene. Participants were asked about their use of over-the-counter and prescription medications at baseline and every 4 months during the trial. Skin follow-up examinations were scheduled annually with a study dermatologist; confirmed lesions were the endpoints in the study. We used a risk set approach to the analysis of grouped times survival data and unconditional logistic regression to compute odds ratios [ORs] for various exposures to NSAIDs. The use of NSAIDs was reported in over 50% of questionnaires. For BCC, NSAIDs exhibited a weak protective effect in crude analyses, which attenuated markedly after adjustment. For SCC, the use of NSAIDs in the year previous to diagnosis reduced the odds by almost 30% (adjusted OR=0.71, 95% CI 0.48-1.04). When we accounted for frequency of use, results for BCC were not striking, and there were inconsistent suggestions of an inverse association with SCC. There were some indications of a modest, nonsignificant reduction on the number of BCCs and SCCs with NSAID use. Our data suggest a weak and inconsistent chemopreventive effect of NSAIDs on BCC and SCC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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".