Humanistic and cost burden of systemic sclerosis: A review of the literature
Bibliographic record
Abstract
BACKGROUND: Systemic sclerosis (SSc), or systemic scleroderma, is a chronic multisystem autoimmune disease characterised by widespread vascular injury and progressive fibrosis of the skin and internal organs. Patients with SSc have decreased survival, with pulmonary involvement as the main cause of death. Current treatments for SSc manage a range of symptoms but not the cause of the disease. Our review describes the humanistic and cost burden of SSc. METHODS: A structured review of the literature was conducted, using predefined search strategies to search PubMed, Embase, and the Cochrane Library. Grey literature searches also were conducted. RESULTS: In total, 2226 articles were identified in the databases and 52 were included; an additional 10 sources were included from the grey literature. The review identified six studies reporting relevant cost estimates conducted in five different countries and four studies that assessed the humanistic burden of SSc. Total direct annual medical costs per patient for Europe varied from €3544 to €8452. For Canada, these costs were reported to be from Can$5038 to Can$10,673. In the United States, the total direct health care costs were reported to be US$17,365 to US$18,396. Different key drivers of direct costs were reported, including hospitalisations, outpatients, and medication. The total annual costs per patient were reported at Can$18,453 in Canada and varied from €11,074 to €22,459 in Europe. Indirect costs represented the largest component of the total costs. EQ-5D utility scores were lower for patients with SSc than those observed in the general population, with reported mean values of 0.49 and 0.68, respectively. The average value of the Health Assessment Questionnaire for patients with SSc was significantly higher than the control population (0.94), and the average value of the SF-36 was significantly lower than the control population: 49.99 for the physical dimension and 58.42 for the mental dimension. CONCLUSIONS: Overall, there is a paucity of information on the burden of SSc. Nonetheless, our review indicates that the quality of life of patients with SSc is considerably lower than that of the general population. In addition, SSc places a considerable economic burden on health care systems and society as a whole.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".