Longterm Work Productivity Costs Due to Absenteeism and Permanent Work Disability in Patients with Early Rheumatoid Arthritis: A Nationwide Register Study of 7831 Patients
Bibliographic record
Abstract
OBJECTIVE: To estimate the development and potential disproportional distribution of longterm productivity costs (PC) and their determinants leading to work absenteeism and permanent work disability in working-aged patients with early rheumatoid arthritis (RA). METHODS: A cohort of subjects with early RA was created by identifying the new cases of RA from the national drug reimbursement register that had been granted a special reimbursement for their antirheumatic medications for RA from 2000-2007. The dataset was enriched by cross-linking with other national registries detailing work absenteeism days and permanent disability pensions. In the base case, the human capital approach was applied to estimate PC based on subjects' annual number of absenteeism days and incomes. Hurdle regression analysis was applied to study the determinants of PC. RESULTS: Among the 7831 subjects with early RA, the mean (bootstrapped 95% CI) annual PC per person-observation year was €4800 (4547-5070). The annual PC declined after the first year of RA diagnosis, but increased significantly in subsequent years. In addition, the PC was heavily disproportionally concentrated in a small fraction of patients with RA, because only around 20% of patients accounted for the majority of total annual PC. The initiation of active drug treatment during the first 3 months after RA diagnosis significantly reduced the cumulative PC when compared with no drug treatment. CONCLUSION: The longterm PC increased significantly in parallel with years elapsing after RA diagnosis. Further, the majority of these PC are incurred by a small proportion of patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".