Practice of Epidemiology Comparison of Self-reported Lifetime Sun Exposure with Two Methods of Cutaneous Microtopography
Bibliographic record
Abstract
There is currently no ‘‘gold standard’ ’ for measuring lifetime sun exposure. Exploration of alternatives to self-reports is important for examining illnesses related to ultraviolet light exposure. Using skin replicas obtained from 184 controls in a breast cancer case-control study (Toronto, Ontario, Canada, 2004–2005), the authors compared self-reported indicators of lifetime sun exposure with two measures of cutaneous microtopography, the Beagley-Gibson system and skin line counts. With the Beagley-Gibson system, significantly increased odds ratios were found for age (odds ratio (OR) 1.10, 95 % confidence interval (CI): 1.05, 1.16), spending 7 days outside per week during the summer (OR 3.33, 95 % CI: 1.48, 7.50), and lifetime number of sunlamp sessions. Significantly decreased odds ratios were found for having darker skin, ever giving birth, and ever using sunlamps. With the skin line count approach, significant positive associations were found for age (OR 2.31, 95 % CI: 1.23, 4.35), age squared, duration of working in outdoor jobs (OR 0.88, 95 % CI: 0.79, 0.98), and average number of outdoor activities per week at ages 20–29 years (OR 1.05, 95 % CI: 1.00, 1.10). While the Beagley-Gibson method was associated with more variables than the skin line count method, both methods require further refinement before graded skin replicas can be recommended as a substitute for self-report measures. data collection; questionnaires; skin; sunlight; validation studies [publication type] Abbreviations: CI, confidence interval; ICC, intraclass correlation coefficient; OR, odds ratio.
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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 | 0.267 | 0.514 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".