Patient-centered Outcomes Research in Interstitial Lung Disease: An Official American Thoracic Society Research Statement
Bibliographic record
Abstract
Abstract Background In the past two decades, many advances have been made to our understanding of interstitial lung disease (ILD) and the way we approach its treatment. Despite this, many questions remain unanswered, particularly those related to how the disease and its therapies impact outcomes that are most important to patients. There is currently a lack of guidance on how to best define and incorporate these patient-centered outcomes in ILD research. Objectives To summarize the current state of patient-centered outcomes research in ILD, identify gaps in knowledge and research, and highlight opportunities and methods for future patient-centered research agendas in ILD. Methods An international interdisciplinary group of experts was assembled. The group identified top patient-centered outcomes in ILD, reviewed available literature for each outcome, highlighted important discoveries and knowledge gaps, and formulated research recommendations. Results The committee identified seven themes around patient-centered outcomes as the focus of the statement. After a review of the literature and expert committee discussion, we developed 28 research recommendations. Conclusions Patient-centered outcomes are key to ascertaining whether and how ILD and interventions used to treat it affect the way patients feel and function in their daily lives. Ample opportunities exist to conduct additional work dedicated to elevating and incorporating patient-centered outcomes in ILD research.
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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 | 0.474 | 0.405 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.015 | 0.031 |
| 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".