An American Thoracic Society Official Research Statement: Future Directions in Lung Fibrosis Research
Bibliographic record
Abstract
BACKGROUND: Pulmonary fibrosis encompasses a group of lung-scarring disorders that occur owing to known or unknown insults and accounts for significant morbidity and mortality. Despite intense investigation spanning decades, much remains to be learned about the natural history, pathophysiology, and biologic mechanisms of disease. PURPOSE: To identify the most pressing research needs in the lung fibrosis community and to provide a roadmap of priorities to investigators, funding agencies, patient advocacy groups, and other interested stakeholders. METHODS: An ad hoc international working group of the American Thoracic Society with experience in clinical, translational, and bench-based research in fibrotic lung diseases was convened. The group used an iterative consensus process to identify successes and challenges in pulmonary fibrosis research. MEASUREMENTS AND MAIN RESULTS: The group identified five main priority areas in which substantial resources should be invested to advance our understanding and to develop novel therapies for patients with pulmonary fibrosis. These priorities include develop newer models of human lung fibrosis, engage current and new stakeholders to provide sustained funding for the initiatives, create a global infrastructure for storing patient-derived materials, establish collaborative preclinical and clinical research networks in fibrotic lung disease, and create a global lung fibrosis initiative that unites these multifaceted efforts into a single virtual umbrella structure. CONCLUSIONS: Despite recent advances in the treatment of some forms of lung fibrosis, many gaps in knowledge about natural history, pathophysiology, and treatment remain. Investment in the research priorities enumerated above will help address these shortcomings and enhance patient care worldwide.
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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 | 0.072 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.031 | 0.036 |
| Insufficient payload (model declined to judge) | 0.035 | 0.040 |
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".