Subsequent primary malignancies in patients with nonmelanoma skin cancer in England: a national record-linkage study.
Bibliographic record
Abstract
BACKGROUND: Conflicting evidence exists about whether people with a history of nonmelanoma skin cancer (NMSC) are at higher risk of subsequent primary malignant cancers than those without. METHODS: An all England record-linked hospital and mortality dataset spanning from 1999 to 2011 was used. We constructed two cohorts: one that comprised people with a history of NMSC (502,490 people), and a control cohort that comprised people without. We "followed up" these two cohorts electronically to determine observed and expected numbers of people with subsequent primary cancers in each, based on person-years at risk, and calculated standardized risk ratios (RR). RESULTS: Comparing the NMSC cohort with the non-NMSC cohort, the RR for all subsequent malignant cancers combined was 1.36 [95% confidence interval (CI), 1.35-1.37]. Significantly increased RRs (P < 0.05) were found for 26 of the 29 cancer types studied, in particular for salivary gland, melanoma, bone, and upper gastrointestinal tract cancers. The RRs were also particularly high when comparing younger people with and without NMSC. CONCLUSIONS: NMSC is strongly associated with a broad spectrum of other primary cancers, particularly in younger age groups. The pattern suggests a genetic or early-acquired etiologic association. IMPACT: These results represent what can be done using very large, linked, routinely collected administrative datasets; but such datasets lack detail. Further work to establish the mechanisms behind these associations is warranted.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".