Patterns of Basal Cell Carcinoma Presentation in the NIH All of Us Database
Bibliographic record
Abstract
PURPOSE: To examine risk factors for the development of eyelid, face, and body basal cell carcinoma (BCC) within the National Institutes of Health All of Us database. METHODS: Around 7342 patients with BCC and 29,728 age-matched controls were included. Patients with genetic syndromes predisposing to BCC, and patients with a history of solid organ transplants were excluded. Outcomes examined included tumor location, ethnicity, age, smoking, alcohol intake, income, and access to care. Univariate and multivariate regression analyses were performed to examine the association between BCC on different areas of the body and race as well as the effect of modifiable risk factors. RESULTS: Almost 48.7% (3623) of patients had BCC on the body (nonface), 47.2% had BCC on the face (3505), and 4.1% (304) had BCC of the eyelid. White race (OR, 11.79; CI, 5.99-27.73; p < 0.001) and male sex (OR, 1.23; CI, 1.14-1.33; p < 0.001) were nonmodifiable risk factors for BCC and overall, patients with facial BCC were significantly older ( p < 0.001) than those with body BCC. Nonwhite patients with BCC were younger, and significantly more likely to have nonfacial BCC ( p < 0.001). On multivariate analysis, alcohol intake also showed a dose-dependent increased risk for BCC (OR, 1.54; CI, 1.14-2.13; p < 0.001) of the face. CONCLUSIONS: Nonwhite patients were significantly younger and more likely to be diagnosed with nonfacial BCC. Thus, we hypothesize that BCC pathogenesis may not be as closely related to cumulative sun exposure as for white patients. Alcohol intake is an important modifiable risk factor.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".