Melanoma survivors are at increased risk for second primary keratinocyte carcinoma
Bibliographic record
Abstract
BACKGROUND: Recent large cohorts have reported that melanoma survivors are at risk of developing second keratinocyte carcinoma (KC). However, the detailed proportion and risk are still unknown. We aimed to comprehensively analyze the risk of developing keratinocyte carcinoma after primary melanoma. METHODS: We conducted systematic literature research in PubMed, Embase, Web of Science, and Cochrane Library published prior to September 13, 2021. Proportion and standardized incidence ratios (SIR) with its corresponding 95% confidence interval (CI) were pooled for assessing the risk. RESULTS: A total of 15 studies encompassing 168,286 patients were included in our analysis. The pooled proportions of melanoma survivors that developed a subsequent basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and KC were 4.11% (95% CI, 1.32-6.90), 2.54% (95% CI, 1.78-3.31), and 5.45% (95% CI, 3.06-7.84), respectively. The risks of developing a second BCC, SCC, and KC in melanoma survivors were 5.3-fold (SIR 5.30; 95% CI, 4.87-5.77), 2.6-fold (SIR 2.58; 95% CI, 1.33-5.04), and 6.2-fold (SIR 6.17; 95% CI, 3.66-10.39) increased in comparison with the general population. Both fixed effects and random effects models were applied in conducting meta-analysis and reached a consistent conclusion. CONCLUSIONS: Our results indicated melanoma survivors are at elevated risk of experiencing second primary BCC and SCC, which suggested the significance of surveillance for second primary KC and efforts for prevention in patients with a history of melanoma.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".