Economic impact of juvenile idiopathic arthritis
Bibliographic record
Abstract
OBJECTIVE: Juvenile idiopathic arthritis (JIA) is a potentially devastating chronic pediatric disease. Although high costs have been well described in adult arthritis, little is known about the economic impact of JIA. Our objective was to describe direct medical costs for children with JIA compared with controls. METHODS: Consecutive clinic attendees (n = 155) with JIA were enrolled from 2 tertiary referral pediatric centers. Outpatient clinic controls without JIA (n = 181) were recruited at the respective centers. Data on direct medical costs were obtained at 3-month intervals. Average annualized direct medical costs were calculated, expressed in 2005 Canadian dollars. RESULTS: The total difference in annualized average direct medical costs for children with JIA versus controls was $1,686 (95% confidence interval $875, $2,500). JIA subjects had substantially higher costs concerning medication use, visits to specialists and allied health care professionals, and diagnostic tests. Multiple linear regression models for the JIA sample revealed that higher active joint count was independently associated with greater total direct medical costs. Also, JIA type was a predictor of greater direct costs, with higher costs for patients with polyarthritis (rheumatoid factor positive or negative) or systemic JIA. CONCLUSION: The economic impact of JIA is substantial, and higher active joint count is independently associated with greater costs. This may be of particular significance given the emergence of new, costly medications for use in JIA. Insights into the relationship between disease activity and cost in JIA should assist policy makers regarding resource allocation in the setting of competing demands. Ultimately, decisions regarding access to therapies should be considered in terms of overall cost-benefit ratios.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".