Idiopathic pulmonary fibrosis: state of the art for 2023
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease characterised by worsening respiratory symptoms and physiological impairment. Increasing awareness of the clinical manifestations of IPF, more widespread use of computed tomography scans and other potential factors have contributed to a rising prevalence of IPF over the last two decades, especially among people over the age of 65 years. Significant advances in the understanding of the pathobiology of IPF have emerged, and multiple genetic and nongenetic contributors have been identified. The individual patient course and the rate of disease progression in IPF are often unpredictable and heterogeneous. The rate of lung function decline is further modified by treatment with antifibrotic therapies, which have been shown to slow down disease progression. The presence of comorbid conditions may increase symptom burden and impact survival. Clinical monitoring at regular intervals to assess for disease progression by worsening symptoms, physiological parameters and/or radiological features is essential to assess the natural disease course and to guide further management, including prompt detection of complications and comorbid conditions that warrant additional treatment considerations, and timely consideration of referral to palliative care and lung transplantation for the appropriate patient. More studies are needed to determine whether early detection of IPF might improve patient outcomes. The purpose of this concise clinical review is to provide an update on IPF diagnosis, epidemiology, natural history and treatment in the context of new knowledge and latest clinical practice guidelines.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".