The effect of etanercept on work productivity in patients with early active rheumatoid arthritis: results from the COMET study
Bibliographic record
Abstract
OBJECTIVES: To compare the impact of the combination of etanercept (ETN) and MTX with MTX alone on work productivity among MTX-naïve patients with active early RA over a 12-month period. METHODS: The COMET (COmbination of Methotrexate and ETanercept) trial was a 2-year double-blind randomized clinical trial. Absenteeism during the first year was measured and it included: (i) number of missed workdays; (ii) reduced working time; and (iii) number of stopped workdays. Each absenteeism measure was estimated using a mixed model, and their variations were estimated by bootstrapping. As a sensitivity analysis, the lost workdays due to presenteeism (reduced performance at work) was also estimated. RESULTS: Two hundred and five patients [MTX (n = 100) vs ETN + MTX (n = 105)], who were working full time or part time at baseline and had at least one follow-up observation, were included in the analysis. Compared with the MTX group, the ETN + MTX group had a maximum of 37 fewer missed workdays or at minimum 22 fewer missed workdays. The associated productivity gain equalled 2586 pounds and 1555 pounds, respectively. When additionally accounting for presenteeism, the total improvement could be as high as 42 (95% CI 16, 69) fewer lost workdays representing a productivity gain of 2968 pounds. CONCLUSIONS: Our results demonstrated that early treatment with ETN + MTX led to a significant attenuation of absenteeism among patients with early active RA. These productivity gains represent benefit beyond the traditional measures of clinical and radiographic improvements. Further research to simultaneously measure both absenteeism and presenteeism is warranted.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".