Effect of occupation on risk of developing MS: an insurance cohort study
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to estimate the occupational risks in relation to multiple sclerosis (MS). The immediate background for this research was our finding that there had been a high number of critical illness insurance claims by patients diagnosed with MS within the agricultural segment of a Danish pension fund. DESIGN: An open insurance cohort. All payouts for the critical illness insurance from 2002 to 2011 were continuously registered. SETTINGS: PensionDanmark; one of Denmark's largest pension funds. PARTICIPANTS: PensionDanmark insures more than 300 000 members of the Danish Confederation of Trade Unions against critical illness. All members are insured, and all policies are identical. The total exposure is 3.3 million person-years. PRIMARY OUTCOME MEASURES: The incidence of MS. RESULTS: During the 10-year period, 389 persons were diagnosed with MS. The crude incidence rate for men was 10.2/100 000; the corresponding figure for women was 16.1/100 000. We found signs of an overall effect of occupation on the risk of developing MS, and the high frequency found within the agricultural segment was attributed to dairy operators, who had an incidence of MS 2.0 times higher than the rest of the study's population (95% CI=1.2 to 3.0). CONCLUSIONS: Our results indicate some occupational risk factors in MS, and this should be investigated further.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".