Detection of Idiopathic Pulmonary Fibrosis Disease Progression using Respiratory Oscillometry
Bibliographic record
Abstract
Background: The prognosis of idiopathic pulmonary fibrosis (IPF) is poor, with mean survival of 3-5 years. Common measures of disease progression are pulmonary function tests (PFTs) and six-minute walk distance (6MWD). However, these tests require access to healthcare facilities with qualified personnel. This is not always feasible for patients in smaller communities. Respiratory oscillometry is a different PFT modality; its metrics of lung stiffness, reactance at 5Hz during inspiration (X5in.) and end-inspiration reactance (XeI) have been found to correlate with IPF severity. Objective: To evaluate whether changes in X5in. and XeI can detect IPF progression. Methods: From September 2019 to December 2022, 230 subjects with IPF diagnosed according to international guidelines were enrolled for paired spectral (5-37Hz) and monofrequency (10Hz) oscillometry before every clinically-indicated routine PFTs and 6MWT. Results: Of the 230 subjects enrolled, 87 (58M/29F; mean±SD age=71.2±7.7 years) exhibited a decline in forced vital capacity (FVC) of ≥10% in on follow-up (mean±SD = 524 ± 260 days). In these 87 patients, we observed significant decline in the diffusing capacity (DLCO: median [IQR] = 11.8 [9.0, 15.1] vs 9.9 [7.9, 13.2] ml/min/mmHg, p=0.016), 6MWD (mean±SD = 465.4±105.6 vs 421.2±112.3 m, p=0.009). Oscillometry revealed significant worsening of both X5in and XeI (median [IQR]) = -2.1 [−2.9, -1.6] vs -2.5 [−3.4, -1.9] cmH2O s/L and -0.6 [−1.0, -0.3] vs -0.8 [−1.3, -0.5] cmH2O s/L, respectively, p<0.05 for both). Conclusion: X5in. and Xel can detect IPF progression and should be used when patients cannot access or perform standard PFTs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".