Estimating the monetary value of the annual productivity gained in patients with early rheumatoid arthritis receiving etanercept plus methotrexate: interim results from the PRIZE study
Bibliographic record
Abstract
OBJECTIVE: To measure and value the impact of combined etanercept (ETN) and methotrexate (MTX) therapy on work productivity in patients with early rheumatoid arthritis (RA) over 52 weeks. METHODS: MTX- and biological-naïve patients with RA (symptom onset ≤12 months; Disease Activity Score based on a 28-joint count (DAS28) >3.2) received open-label ETN50/MTX for 52 weeks. The Valuation of Lost Productivity (VOLP) questionnaire, measuring paid and unpaid work productivity impacts, was completed approximately every 13 weeks. Bootstrapping methods were used to test changes in VOLP outcomes over time. One-year productivity impacts were compared between responders (DAS28 ≤3.2) at week 13 and non-responders using zero-inflated models for time loss and two-part models for total costs of lost productivity. RESULTS: 196 patients were employed at baseline and had ≥1 follow-up with VOLP. Compared with baseline, at week 52, patients gained 33.4 h per 3 months in paid work and 4.2 h per week in unpaid work. Total monetary productivity gains were €1322 per 3 months. Over the 1-year period, responders gained paid (231 h) and unpaid work loss (122 h) compared with non-responders, which amounted to a gain of €3670 for responders. CONCLUSIONS: This is the first clinical trial to measure and value the impact of biological treatment on all the labour input components that affect overall productivity. Combination therapy with ETN50/MTX was associated with a significant productivity gain for patients with early RA who were still observed at week 52. Over the 1-year treatment period, responders at week 13 suffered significantly less productivity loss than non-responders suggesting this gain was related to treatment response. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov number NCT00913458.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".