A meta-analysis of melanoma risk in industrial workers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Industrial workers are exposed to occupational pollutants, which may cause diseases such as cancer, but links to melanoma are not established. The identification of industry-related risk factors for melanoma incidence and mortality might be of importance for workers, health providers, and insurance companies. To assess melanoma incidence and mortality among oil/petroleum, chemical, and electrical industry workers. All studies reporting standardized mortality ratios (SMR) and/or standardized incidence ratios (SIR) of melanoma in workers employed in oil/petroleum, chemical, and electrical industries were included. Random-effect meta-analyses were carried out to summarize SIR and SMR for melanoma among oil/petroleum, chemical, and electrical industry workers. Heterogeneity was assessed using χ and I statistics. Possible source bias and quality were assessed using the Strengthening the Reporting of Observational Studies in Epidemiology checklist and a modified version of the Newcastle-Ottawa scale. Of 1878 citations retrieved, we meta-analyzed 21, 6, and 9 studies for the oil/petroleum, electrical, and chemical industry, respectively. Oil/petroleum industry: summary standardized incidence ratio (SSIR) = 1.23 [95% confidence interval (CI): 1.11-1.36, I = 45%]; summary standardized mortality ratio (SSMR) = 1.02 (95% CI: 0.81-1.28, I = 48%); subgroups: SSIR = 1.16 (95% CI: 1.01-1.32, I = 15%), SSMR = 1.19 (95% CI: 1.00-1.42, I = 20%). Electrical industry: SSIR = 1.00 (95% CI: 0.93-1.11, I = 72%); SSMR = 1.16 (95% CI: 0.74-1.81, I = 11%). Chemical industry: SSIR = 2.08 (95% CI: 0.47-9.24, I = 73%); SSMR = 2.01 (95% CI: 1.09-3.72, I = 33%). Our meta-analysis suggests a slightly increased risk of developing melanoma among oil/petroleum industry workers and an increased melanoma mortality among oil/petroleum and chemical industry workers. No increased risks were found among electrical industry workers.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it