Descriptive characteristics of occupational exposures and medical follow-up in the cohort of workers of the Siberian Group of Chemical Enterprises in Seversk, Russia
Bibliographic record
Abstract
PURPOSE: To date, only a few studies have examined long-term health risks of exposures in the uranium processing industry and reported contradictory results, necessitating further research in this area. This is the first description of a cohort of ∼65,000 uranium processing workers (20.6% women) of the Siberian Group of Chemical Enterprises (SGCE) in Seversk, Russia, first employed during 1950-2010. METHODS: SGCE is one of the largest and oldest uranium processing complexes in the world. SGCE workers at the Radiochemical, Plutonium, Sublimate and Enrichment plants were exposed to a combination of internal and external radiation, while workers at the Support Facility were primarily exposed to non-radiation factors. RESULTS: Mean cumulative gamma-ray dose based on individual external dosimetry was 28.3 millisievert. About 4,000 workers have individual biophysical survey data that could be used for estimation of organ doses from uranium. SGCE workers were followed up for mortality and cancer incidence during 1950-2013 (vital status known for 80.8% of workers). The SGCE computerized database contains information on the results of regular medical examinations, and on smoking, alcohol and other individual characteristics. CONCLUSIONS: The SGCE cohort is uniquely suited to examine long-term health risks of exposures to gamma-radiation and long-lived radionuclides in uranium processing workers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".