8289864 Comparative analysis of mesothelioma by occupation in South Korea and the UK
Bibliographic record
Abstract
Objective Even though South Korea has a significant history of asbestos use, there are fewer cases of malignant mesothelioma in South Korea than in the UK. The purpose of this study is to describe the occupational distribution of samples collected from mesothelioma cases in South Korea and the UK, where the incidence of mesothelioma appears to be different using different surveillant schemes. Material and Methods The following three data sources were matched by unifying the occupational classification of cases reported as mesothelioma to a two-digit Standard Occupational Classification (SOC) code: UK Industrial Injuries Disablement Benefit (IIDB) mesothelioma SOC classification table from 2017 to the third quarter of 2020, cases with diagnosis code C45 reported by The Health and Occupation Research (THOR) network from 2017 to 2021, and KCOMWEL (Korea Workers’ Compensation & Welfare Service) occupational disease investigation cases with diagnosis code C45 during the same period. We compared the distribution of occupational groups by each source and calculated the standardised rate ratio (SRR) for each occupational group based on the population distribution by occupation surveyed in the UK in 1981 and South Korea in 1980. Results In the SOC distribution, the three datasets showed differences in the distribution only in 81 (machine operators). The 53 (construction occupations) with the highest number reported cases had a high SRR in the UK. A statistically significant SRR was also reported in South Korea, but it was lower than in the UK. Conclusion Surveillance data (THOR) shows a similar distribution to compensation data (IIDB). Mesothelioma may have been underreported among Korean construction workers, but this may be a diluted result due to the lack of detailed census data. Additional data is needed to estimate the number of construction workers in the 1980s for a more accurate comparison, especially in Korea.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".