Exploring small airways disease in IPF patients: insights from oscillometry analysis
Bibliographic record
Abstract
Introduction: Small airway disease (SAD) in idiopathic pulmonary disease (IPF) has not been extensively studied. Aims: To investigate SAD in IPF patients by the means of oscillometry. Methods: In the IPF group, specialists recorded demographics, time from first diagnosis, antifibrotic treatment and treatment period, as well as detailed lung function including oscillometry using TremoFlo® c-100 (THORASYS Thoracic Medical Systems Inc., Montreal, QC, Canada), spirometry (Spirolab, MIR, Rome, Italy), and plethysmography (CardinalHealth, Yorba Linda, CA, USA). Results: A total of 48 IPF individuals, 69% were male, with an average age of 72±2 years, were included. The participants consisted of ex-smokers (63%), non-smokers (33%), and current smokers (6%) with an average of 47±38 pys. Approximately 54% of the patients received antifibrotic treatment (48% taking pirfenidone and 52% nintedanib). The mean R5, R5-20, AX, X5, Fres were 3±1 cmH2O/L/s, 0.5±0.4 cmH2O/L/s, 1.2±0.8 cmH2O/L/s, -0.18±0.07 cmH2O/L/s and 20±3.4 Hz, respectively. The study found increased R5, R5-R20, X5, and Fres values compared to literature ranges for healthy individuals measured with Tremoflo [1, 2], similar to those found in COPD patients [2], not detected with spirometry or plethysmography. Antifibrotic treatment was found to have no significant effect on lung function parameters, and smoking status was not associated with any changes in these parameters. Conclusions: IPF patients showed higher levels of central, and peripheral obstruction, and reactance in comparison to the normal ranges reported in the literature. [1] Goebel I et al. Toxics. 2023;11:758. [2] Lundblad LKA et al. Sci Rep. 2019;9:11618.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".