Socioeconomic disparity in the natural history of cutaneous melanoma: evidence from two large prospective cohorts
Bibliographic record
Abstract
BACKGROUND: Previous studies on the associations between socioeconomic status (SES) and cutaneous malignant melanoma (CMM) failed to distinguish the effects of different SES factors under an individual-data-based prospective study design. METHODS: Based on UK Biobank (UKB) and China Kadoorie Biobank (CKB), we estimated the effects of four SES factors on transitions from baseline to CMM in situ, subsequently to invasive CMM and further CMM mortality by applying multistate models. We further explored to which extent the associations between SES and CMM incidence could be explained by potential mediators including sun exposure, lifestyle and ageing in UKB. RESULTS: In multistate analyses, good household income was independently associated with an increased risk of CMM in situ (HR=1.38, 95% CI: 1.21 to 1.58) and invasive CMM (HR=1.34, 95% CI: 1.22 to 1.48) in UKB. These findings were partly validated in CKB. Especially in UKB, we observed an increased risk of CMM in situ and invasive CMM among participants with good type of house; only good education was independently associated with lower risk of evolving to invasive CMM among patients with CMM in situ (HR=0.69, 95% CI: 0.52 to 0.92); only good household income was independently associated with lower risk of CMM mortality among patients with CMM (HR=0.65, 95% CI: 0.45 to 0.95). In mediation analysis, the proportions attributable to the mediating effect were <6% for all selected variables, including self-reported sun exposure-related factors. CONCLUSION: SES factors have different effects on the incidence and progression of CMM. The association between SES and incident CMM is neither causal nor well explained by selected mediators.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".