Predictive value of European Scleroderma Group Activity Index in an early scleroderma cohort
Bibliographic record
Abstract
Objective: To estimate the effect of disease activity, as measured by the European Scleroderma Research Group Activity Index (EScSG-AI), on the risk of subsequent organ damage in a large systemic sclerosis (SSc) cohort. Methods: Of 421 SSc patients from the Canadian Scleroderma Research Group database with disease duration of ⩽ 3 years, 197 who had no evidence of end-stage organ damage initially and available 3 year follow-up were included. Disease activity was assessed by the EScSG-AI with two variability measures: the adjusted mean EScSG-AI (the area under the curve of the EScSG-AI over the observation period) and persistently active disease/flare. Outcomes were based on the Medsger severity scale and included accrual of a new severity score (Δ ⩾ 1) overall and within organ systems or reaching a significant level of deterioration in health status. Results: After adjustment for covariates, the adjusted mean EScSG-AI was the most consistent predictor of risk across the study outcomes over 3 years in dcSSc: disease progression defined as Δ ⩾ 1 in any major internal organ, significant decline in forced vital capacity and diffusing capacity of carbon monoxide, severity of visceral disease and HAQ Disability Index worsening. In multivariate analysis, progression of lung disease was predicted solely by adjusted mean EScSG-AI, while the severity of lung disease was predicted the adjusted mean EScSG-AI, older age, modified Rodnan skin score (mRSS) and initial severity. The EScSG-AI was associated with patient- and physician-assessed measures of health status and overpowered the mRSS in predicting disease outcomes. Conclusion: Disease activity burden quantified with the adjusted mean EScSG-AI predicted the risk of deterioration in health status and severe organ involvement in dcSSc. The EScSG-AI is more responsive when done repeatedly and averaged.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".