Reconciling Healthcare Professional and Patient Perspectives in the Development of Disease Activity and Response Criteria in Connective Tissue Disease–related Interstitial Lung Diseases
Bibliographic record
Abstract
Interstitial lung diseases (ILD), including those related to connective tissue disease (CTD), and idiopathic pulmonary fibrosis (IPF) carry high morbidity and mortality. Great efforts are under way to develop and investigate meaningful treatments in the context of clinical trials. However, efforts have been challenged by a lack of validated outcome measures and by inconsistent use of measures in clinical trials. Lack of consensus has fragmented effective use of strategies in CTD-ILD and IPF, with a history of resultant difficulties in obtaining agency approval of treatment interventions. Until recently, the patient perspective to determine domains and outcome measures in CTD-ILD and IPF had never been applied. Efforts described here demonstrate unequivocally the value and influence of patient involvement on core set development. Regarding CTD-ILD, this is the first OMERACT working group to directly address a manifestation/comorbidity of a rheumatic disease (ILD) as well as a disease not considered rheumatic (IPF). The OMERACT 11 proceedings of the CTD-ILD Working Group describe the forward and lateral process to include both the medical and patient perspectives in the urgently needed identification of a core set of preliminary domains and outcome measures in CTD-ILD and IPF.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".