Measures of Cumulative Exposure from a Standardized Sun Exposure History Questionnaire: A Comparison with Histologic Assessment of Solar Skin Damage
Bibliographic record
Abstract
Ultraviolet radiation exposure is the dominant environmental determinant of all major forms of skin cancer; however, the nature of the association is incompletely understood. Existing instruments to capture sun exposure history tend to yield reproducible results, but the validity of these responses is unknown. To address this question, the authors examined the relation between responses to a standardized sun exposure instrument and histologic evidence of actinic damage in a population-based study of keratinocyte cancers from New Hampshire diagnosed from July 1, 1997, through March 31, 2000. A single study dermatopathologist histologically reviewed the adjacent skin of 925 skin cancer biopsies for the presence of solar keratoses and the extent of solar elastosis. The authors compared these measures with responses to a personal interview on history of sunburns, sunbathing, and time spent outdoors. Focusing on site-specific exposure, they found variables that estimated cumulative exposure related to histologic evidence of actinic damage. In contrast, measures of acute/intermittent exposure were generally unrelated to solar damage histologically. Findings suggest that cumulative, but not intermittent, measures of sun exposure derived from a personal interview appear to reflect a person's exposure history based on histologic evidence.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".