Discordance between Patient and Physician Assessments of Disease Severity in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To describe the magnitude and correlates of discordance between patient and physician assessments of disease severity in patients with systemic sclerosis (SSc). METHODS: Subjects were patients enrolled in the Canadian Scleroderma Research Group Registry. The outcomes of interest were patient and physician global assessments of disease severity (scales ranging from 0-10). Predictors of disease severity represented the spectrum of disease in SSc (skin involvement, severity of Raynaud's phenomenon, shortness of breath, gastrointestinal symptoms and pain, number of fingertip ulcers, tender and swollen joints, creatinine, and fatigue). The results of the analysis were validated in an independent sample of patients with SSc from the United States. RESULTS: Patients perceived greater disease severity than physicians (mean difference 0.78 ± 2.65). The agreement between patient and physician assessments of disease severity was, at best, modest (intraclass correlation 0.3774; weighted κ 0.3771). Although both patients and physicians were influenced by skin scores, breathlessness, and pain, the relative importance of these predictors differed. Patients were also influenced by other subjective symptoms, while physicians were also influenced by disease duration and creatinine. The predictors explained 56% of the deviance in the patient global assessments and 29% in the physician assessments. These findings were confirmed in the US dataset. CONCLUSION: Patients and physicians rate SSc disease severity differently in magnitude and are influenced by different factors. Patient-assessed and physician-assessed measures of severity should be considered as complementary and used together in future studies of SSc.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".