Effect of Rheumatoid Arthritis on Longterm Sickness Absence in 1994-2011: A Danish Cohort Study
Bibliographic record
Abstract
OBJECTIVE: By linkage of national registries, we investigated the risk of longterm sickness absence (LTSA) ≥ 3 weeks in a large cohort of Danish patients with rheumatoid arthritis (RA) and non-patients. The study aimed to (1) estimate the risk of LTSA for patients with RA compared with the general population, (2) examine whether the risk of LTSA has changed in recent years, and (3) evaluate the effect of other risk factors for LTSA (e.g., physical work demands, age, sex, education, and psychiatric and somatic comorbidities). METHODS: A total of 6677 patients with RA aged 18-59 years in the years 1994-2011 were identified in registries and compared with 56,955 controls from the general population matched by age, sex, and city size. The risk of LTSA was analyzed using Cox proportional hazards models with late entry, controlling for other risk factors and assuming separate risks in the first year after diagnosis and the following years. RESULTS: Compared with the general population, patients with RA had increased risk of LTSA in the first year after diagnosis (HR 5.4 during 1994-1999, 95% CI 4.2-6.8) and in following years (HR 2.4, 95% CI 2.1-2.8). For established RA (> 1 yr after diagnosis), the excess was 20% lower in 2006-2011 (HR 1.9, 95% CI 1.7-2.2) compared with 1994-1999 (p < 0.001). For patients with RA and controls, older age, shorter education, a physically demanding job, and somatic and/or psychiatric comorbidities increased the risk of LTSA. CONCLUSION: While improvements were observed from 1994-1999 to 2006-2011, patients with RA have significant increased risk of LTSA, in particular in the first year after diagnosis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".