Patient-reported outcomes and patient-reported outcome measures in interstitial lung disease: where to go from here?
Bibliographic record
Abstract
Patient-reported outcome measures (PROMs), tools to assess patient self-report of health status, are now increasingly used in research, care and policymaking. While there are two well-developed disease-specific PROMs for interstitial lung diseases (ILD) and idiopathic pulmonary fibrosis (IPF), many unmet and urgent needs remain. In December 2019, 64 international ILD experts convened in Erice, Italy to deliberate on many topics, including PROMs in ILD. This review summarises the history of PROMs in ILD, shortcomings of the existing tools, challenges of development, validation and implementation of their use in clinical trials, and the discussion held during the meeting. Development of disease-specific PROMs for ILD including IPF with robust methodology and validation in concordance with guidance from regulatory authorities have increased user confidence in PROMs. Minimal clinically important difference for bidirectional changes may need to be developed. Cross-cultural validation and linguistic adaptations are necessary in addition to robust psychometric properties for effective PROM use in multinational clinical trials. PROM burden of use should be reduced through appropriate use of digital technologies and computerised adaptive testing. Active patient engagement in all stages from development, testing, choosing and implementation of PROMs can help improve probability of success and further growth.
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".