The cost of systemic sclerosis
Bibliographic record
Abstract
OBJECTIVE: To assess costs related to systemic sclerosis (SSc) and their determinants. METHODS: The Canadian Scleroderma Research Group is comprised of 15 centers contributing to a registry of adult patients with SSc. Available cross-sectional data included clinical variables and standardized measures of health resource use and time loss. Annualized averages of direct medical costs were calculated by multiplying health service utilization levels by the appropriate unit prices, determined from government fee schedules, professional associations, and other sources. Indirect costs were calculated from the subjects' self-reported time loss related to illness and to seeking health care, as well as caregiver time losses. Costs were represented in 2007 Canadian dollars. RESULTS: In the sample of 457 patients with SSc, the average direct cost per patient was $5,038 per year (95% confidence interval [95% CI] $4,400, $5,676). Regarding indirect costs, the value of potential productivity loss related to paid labor was estimated at an average of $5,345 per patient per year (95% CI $4,598, $6,092), and the cost of lost productivity related to unpaid labor contributed another $8,070 per patient annually. The average total annual cost was estimated at $18,453 (95% CI $16,598, $20,308) per patient. Total annual costs were strongly associated with younger age, greater disease severity, and poorer health status. CONCLUSION: Costs related to SSc are considerable, and there is a high impact of disease severity and health status on economic burden.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".