Direct Cost-Modeling of Rheumatoid Arthritis According to Disease Activity Categories in France
Bibliographic record
Abstract
OBJECTIVE: The objective of this cost-of-illness study was to assess the use of direct medical resources, excluding drug costs, by patients with rheumatoid arthritis (RA) in France, and to construct cost estimates according to level of disease activity. METHODS: Three categories of RA disease activity were defined according to Disease Activity Score 28-joint count (DAS28) thresholds: remission (DAS28 < 2.6), low disease activity state (LDAS; i.e., DAS28 ≤ 3.2), and moderate to high disease activity (MHDAS; i.e., DAS28 > 3.2). Eight resource utilization items were defined: medical visits, laboratory tests, hospitalization, imaging, physiotherapy, nursing, adaptive aids, and transportation. Resource utilization and unit costs from the national-payer perspective were estimated through expert opinion and simulated using distribution ranges for each item. Cost distributions were computed by Monte-Carlo simulations estimating overall costs per 6 months over a 2-year period. RESULTS: For patients achieving remission, costs were estimated at a mean of €771 (SD 199) for the first 6 months and at €511 (SD 162) for each subsequent 6-month period. For patients achieving LDAS, costs were estimated at €905 (SD 263) for the first 6 months and €696 (SD 240) for each subsequent 6-month period. For patients in MHDAS, costs were estimated at €1215 per 6 months (SD 405). CONCLUSION: This cost-of-illness assessment provided current estimates of direct medical costs for RA according to disease activity in France. The findings suggest that achieving remission or LDAS is associated with substantially lower medical costs for RA versus being in MHDAS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".