Occupational Risk Factors for Systemic Lupus Erythematosus: A Nationwide Study Based on Hospitalizations in Sweden
Bibliographic record
Abstract
OBJECTIVE: To investigate possible associations between occupation and hospitalization for systemic lupus erythematosus (SLE) in a nationwide study. METHODS: A nationwide database was constructed in Sweden by linking the Swedish Census to the Hospital Discharge Register to obtain data on all first hospitalizations with a primary or secondary diagnosis of SLE in adults during the study period (1970 to 2008). Standardized incidence ratios (SIR) and 95% CI were calculated for different occupations. Two cohorts were defined based on occupational titles recorded in Swedish census data in 1970 and 1980. RESULTS: A total of 8921 male and 42290 female hospitalizations for SLE were retrieved in individuals aged over 15 years. High education (> 12 yrs) was associated with a lower risk of hospitalization for SLE among both women (SIR = 0.73) and men (SIR = 0.72). Among men with the same occupation in 2 consecutive censuses, increased risks (SIR) > 2.0 were present among artistic workers (2.52); shop managers and assistants (3.63); miners and quarry workers (6.04); shoe and leather workers (6.93); plumbers (2.21); other construction workers (2.08); glass, ceramic and tile workers (4.43); chimney sweeps (4.54); and military personnel (3.01). Among women with the same occupation in 2 consecutive censuses, no occupation was associated with SIR > 2.0. CONCLUSION: Occupation may carry significantly increased risk of hospital admission for SLE. Especially among men, several occupations were associated with increased risks for SLE.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".