Treatable traits: a comprehensive precision medicine approach in interstitial lung disease
Bibliographic record
Abstract
Interstitial lung disease (ILD) is a diverse group of inflammatory and fibrotic lung conditions causing significant morbidity and mortality. A multitude of factors beyond the lungs influence symptoms, health-related quality of life, disease progression and survival in patients with ILD. Despite an increasing emphasis on multidisciplinary management in ILD, the absence of a framework for assessment and delivery of comprehensive patient care poses challenges in clinical practice. The treatable traits approach is a precision medicine care model that operates on the premise of individualised multidimensional assessment for distinct traits that can be targeted by specific interventions. The potential utility of this approach has been described in airway diseases, but has not been adequately considered in ILD. Given the similar disease heterogeneity and complexity between ILD and airway diseases, we explore the concept and potential application of the treatable traits approach in ILD. A framework of aetiological, pulmonary, extrapulmonary and behavioural and lifestyle treatable traits relevant to clinical care and outcomes for patients with ILD is proposed. We further describe key research directions to evaluate the application of the treatable traits approach towards advancing patient care and health outcomes in ILD.
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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 | 0.041 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| 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".