Healthcare utilization and economic burden in systemic sclerosis: a systematic review
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is characterized by vasculopathy, fibrosis of skin and internal organs, and autoimmunity with complications including interstitial lung disease, pulmonary hypertension, and digital ulcers with substantial morbidity and disability. Patients with SSc may require considerable healthcare resources with economic impact. The purpose of this systematic review was to provide a narrative synthesis of the economic impact and healthcare resource utilization associated with SSc. METHODS: MEDLINE and EMBASE were searched from inception to 20 January 2021. Studies were included if they provided information regarding the total, direct and indirect cost of SSc. The cost of SSc subtypes and associated complications was determined. Risk of bias assessments through the Joanna Briggs Institute cross-sectional and case series checklists, and the Newcastle-Ottawa Cohort and Case-Control study scales were performed. A narrative synthesis of included studies was planned. RESULTS: The number of publications retrieved was 1778, of which 34 were included representing 20 cross-sectional, 11 cohort, and three case-control studies. Studies used various methods of calculating cost including prevalence-based cost-of-illness approach and health resource units cost analysis. Overall SSc total annual cost ranged from USD $14 959 to $23 268 in USA, CAD $10 673 to $18 453 in Canada, €4607 to €30 797 in Europe, and AUD $7060 to $11 607 in Oceania. Annual cost for SSc-associated interstitial lung disease and pulmonary hypertension was USD $31 285-55 446 and $44 454-63 320, respectively. CONCLUSION: Cost-calculation methodology varied greatly between included studies. SSc represents a significant patient and health resource economic burden. SSc-associated complications increase economic burden and are variable depending on geographical location and access.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".