Immune Inflammation and Disease Progression in Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
The clinical course in idiopathic pulmonary fibrosis (IPF) is highly heterogeneous, with some patients having a slow progression and others an accelerated clinical and functional decline. This study aims to clinically characterize the type of progression in IPF and to investigate the pathological basis that might account for the observed differences in disease behavior. Clinical and functional data were analyzed in 73 IPF patients, followed long-time as candidates for lung transplantation. The forced vital capacity (FVC) change/year (< or ≥10% predicted) was used to define "slow" or "rapid" disease progression. Pathological abnormalities were quantified in the explanted lung of 41 out of 73 patients undergoing lung transplantation. At diagnosis, slow progressors (n = 48) showed longer duration of symptoms and lower FVC than rapid progressors (n = 25). Eleven slow and 3 rapid progressors developed an acute exacerbation (AE) during follow-up. Quantitative lung pathology showed a severe innate and adaptive inflammatory infiltrate in rapid progressors, markedly increased compared to slow progressors and similar to that observed in patients experiencing AE. The extent of inflammation was correlated with the yearly FVC decline (r = 0.52, p = 0.005). In conclusion an innate and adaptive inflammation appears to be a prominent feature in the lung of patients with IPF and could contribute to determining of the rate of disease progression.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".