Summed and Weighted Summary Scores for the Medsger Disease Severity Scale Compared with the Physician’s Global Assessment of Disease Severity in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To develop a weighted summary score for the Medsger Disease Severity Scale (DSS) and to compare its measurement properties with those of a summed DSS score and a physician's global assessment (PGA) of severity score in systemic sclerosis (SSc). METHODS: Data from 875 patients with SSc enrolled in a multisite observational research cohort were extracted from a central database. Item response theory was used to estimate weights for the DSS weighted score. Intraclass correlation coefficients (ICC) and convergent, discriminative, and predictive validity of the 3 summary measures in relation to patient-reported outcomes (PRO) and mortality were compared. RESULTS: Mean PGA was 2.69 (SD 2.16, range 0-10), mean DSS summed score was 8.60 (SD 4.02, range 0-36), and mean DSS weighted score was 8.11 (SD 4.05, range 0-36). ICC were similar for all 3 measures [PGA 6.9%, 95% credible intervals (CrI) 2.1-16.2; DSS summed score 2.5%, 95% CrI 0.4-6.7; DSS weighted score 2.0%, 95% CrI 0.1-5.6]. Convergent and discriminative validity of the 3 measures for PRO were largely similar. In Cox proportional hazards models adjusting for age and sex, the 3 measures had similar predictive ability for mortality (adjusted R(2) 13.9% for PGA, 12.3% for DSS summed score, and 10.7% DSS weighted score). CONCLUSION: The 3 summary scores appear valid and perform similarly. However, there were some concerns with the weights computed for individual DSS scales, with unexpected low weights attributed to lung, heart, and kidney, leading the PGA to be the preferred measure at this time. Further work refining the DSS could improve the measurement properties of the DSS summary scores.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".