Oral prednisone use and risk of keratinocyte carcinoma in non‐transplant population. The VATTC trial
Bibliographic record
Abstract
BACKGROUND: Glucorticosteroids (GC) are potent anti-inflammatory medications with immunosuppressive property. Few retrospective studies have reported the increased risk of development of basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) associated with GC use. OBJECTIVE: We aimed to assess the effect of oral GC use on the risk of BCC and SCC using prospective data. METHODS: We analysed data from the Veterans Affairs Topical Tretinoin Chemoprevention Trial, which followed up patients from 1998 to 2004. Exposure to oral GCs was defined as (1) use of any oral GCs at any point during follow-up and (2) use of GCs for a month or longer. Outcome was occurrence of new BCC or SCC. Cox proportional hazard models were used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: Among the 1051 study participants, 148 patients (14%) had prednisone prescription filled during study period, and 63 (6%) used prednisone for over a month. A total of 472 patients (45%) developed at least one BCC during study: 394 (44%) among non-users of prednisone and 78 (53%) among any time users. The total number of new SCC was 309 (29%): 258 (29%) among non-users of prednisone and 51 (34%) among users. Among any time prednisone users, the adjusted HR was 1.11 (95% CI, 0.87-1.42) for BCC, and 1.05 (95% CI, 0.76-1.45) for SCC. Among those who used prednisone for 30 or more days, the HR was 1.26 (95% CI, 0.90-1.78) for BCC, and 1.03 (95% CI, 0.66-1.60) for SCC. CONCLUSION: This study does not support the existence of association between use of oral GCs and risk of BCC or SCC.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".