Development and preliminary validation of the Sjögren's Tool for Assessing Response (STAR): a consensual composite score for assessing treatment effect in primary Sjögren's syndrome
Bibliographic record
Abstract
OBJECTIVE: To develop a composite responder index in primary Sjögren's syndrome (pSS): the Sjögren's Tool for Assessing Response (STAR). METHODS: To develop STAR, the NECESSITY (New clinical endpoints in primary Sjögren's syndrome: an interventional trial based on stratifying patients) consortium used data-driven methods based on nine randomised controlled trials (RCTs) and consensus techniques involving 78 experts and 20 patients. Based on reanalysis of rituximab trials and the literature, the Delphi panel identified a core set of domains with their respective outcome measures. STAR options combining these domains were proposed to the panel for selection and improvement. For each STAR option, sensitivity to change was estimated by the C-index in nine RCTs. Delphi rounds were run for selecting STAR. For the options remaining before the final vote, a meta-analysis of the RCTs was performed. RESULTS: The Delphi panel identified five core domains (systemic activity, patient symptoms, lachrymal gland function, salivary gland function and biological parameters), and 227 STAR options combining these domains were selected to be tested for sensitivity to change. After two Delphi rounds, a meta-analysis of the 20 remaining options was performed. The candidate STAR was then selected by a final vote based on metrological properties and clinical relevance. CONCLUSION: The candidate STAR is a composite responder index that includes all main disease features in a single tool and is designed for use as a primary endpoint in pSS RCTs. The rigorous and consensual development process ensures its face and content validity. The candidate STAR showed good sensitivity to change and will be prospectively validated by the NECESSITY consortium in a dedicated RCT.
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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 | 0.161 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".