Associations of Cumulative Sun Exposure and Phenotypic Characteristics with Histologic Solar Elastosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Solar elastosis adjacent to melanomas in histologic sections is regarded as an indicator of sun exposure, although the associations of UV exposure and phenotype with solar elastosis are yet to be fully explored. METHODS: The study included 2,589 incident primary melanoma patients with assessment of histologic solar elastosis in the population-based Genes, Environment, and Melanoma study. Ambient erythemal UV (UVE) at places of residence and sun exposure hours, including body site-specific exposure, were collected. We examined the association of cumulative site-specific and non-site-specific sun exposure hours and ambient UVE with solar elastosis in multivariable models adjusted for age, sex, center, pigmentary characteristics, nevi, and, where relevant, body site. RESULTS: Solar elastosis was associated most strongly with site-specific UVE [odds ratio (OR) for top exposure quartile, 5.20; 95% confidence interval (95% CI), 3.40-7.96; P for trend <0.001] and also with site-specific sun exposure (OR for top quartile, 5.12; 95% CI, 3.35-7.83; P for trend <0.001). Older age (OR at >70 years, 7.69; 95% CI, 5.14-11.52; P for trend < 0.001) and having more than 10 back nevi (OR, 0.77; 95% CI, 0.61-0.97; P = 0.03) were independently associated with solar elastosis. CONCLUSION: Solar elastosis had a strong association with higher site-specific UVE dose, older age, and fewer nevi. IMPACT: Solar elastosis could be a useful biomarker of lifetime site-specific UV. Future research is needed to explore whether age represents more than simple accumulation of sun exposure and to determine why people with more nevi may be less prone to solar elastosis.
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.
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 | 0.001 |
| 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 it