Connective Tissue Disease-associated Interstitial Lung Diseases (CTD-ILD) — Report from OMERACT CTD-ILD Working Group
Bibliographic record
Abstract
OBJECTIVE: Interstitial lung disease (ILD) is common in connective tissue disease (CTD) and is the leading cause of mortality. Investigators have used certain outcome measures in randomized controlled trials (RCT) in CTD-ILD, but the lack of a systematically developed, CTD-specific index that captures all measures relevant and meaningful to patients with CTD-ILD has left a large and conspicuous gap in CTD-ILD research. METHODS: The CTD-ILD working group, under the aegis of the Outcome Measures in Rheumatology (OMERACT) initiative, has completed a consensus group exercise to reach harmony on core domains and items for inclusion in RCT in CTD-ILD. During the OMERACT 12 meeting, consensus was sought on domains and core items for inclusion in RCT. In addition, consensus was pursued on a definition of response in RCT. Consensus was defined as ≥ 75% agreement among the participants. RESULTS: OMERACT 12 participants endorsed the domains with minimal modifications. Clinically meaningful progression for CTD-ILD was proposed as ≥ 10% relative decline in forced vital capacity (FVC) or ≥ 5% to < 10% relative decline in FVC and ≥ 15% relative decline in DLCO. CONCLUSION: There is consensus on domains for inclusion in RCT in CTD-ILD and on a definition of clinically meaningful progression. Data-driven approaches to validate these results in different cohorts and RCT are needed.
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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 | 0.038 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".