Development and Initial Validation of the Novel Scleroderma Clinical Trials Consortium Activity Index
Bibliographic record
Abstract
OBJECTIVE: Accurate measurement of disease activity in systemic sclerosis (SSc) remains a significant clinical challenge. The Scleroderma Clinical Trials Consortium (SCTC) convened an Activity Index (AI) Working Group (WG) to develop a novel measure of disease activity (SCTC-AI). METHODS: Using consensus methodology, we developed a conceptual definition of disease activity. Literature review and expert consensus generated provisional SCTC-AI items, which were reduced by Delphi survey. Provisional items were weighted against a combined endpoint of morbidity and mortality, using time-dependent Cox proportional hazards regression analysis of the Australian Scleroderma Cohort Study (ASCS) (n = 1,254). External validation of the SCTC-AI was performed using data collected from 1,103 Canadian Scleroderma Research Group Study participants. RESULTS: Disease activity in SSc was defined using consensus methodology as "aspects of disease that are reversible, or can be arrested, with time and, or effective therapy." One-hundred and forty-one provisional SCTC-AI items were generated and reduced using three rounds of Delphi survey and statistical reduction and weighting, against mortality and quality of life measures, yielding a final 24-item index with a maximum possible score of 140. Survival analysis in an external cohort showed a graded relationship between disease activity scores and survival (P < 0.01). CONCLUSION: We present a novel instrument to quantify the burden of disease activity in SSc. We have employed a rigorous consensus-based process in combination with data-driven methods to develop an instrument that has face, content, and criterion validity. Further work is required to fully validate and confirm the construct and discriminative validity of the SCTC-AI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.157 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".