Characterize Idiopathic Pulmonary Fibrosis using Respiratory Oscillometry
Bibliographic record
Abstract
Introduction: Idiopathic pulmonary fibrosis (IPF) is one of the most common forms of Intersitial Lung Disease. The degree of fibrosis may correlate with reduction in lung compliance. Patients are typically monitored with pulmonary function tests (PFTs), however, these are insensitive to peripheral airways dysfunction, provide limited information on lung compliance and airways resistance, and may be insensitive to detect disease progression. Respiratory oscillometry (Osc) performed during tidal breathing, measures total respiratory impedance, composed of resistance and reactance (X) at multiple wavelengths provides a comprehensive metric of respiratory mechanics and dynamic lung compliance. Objective: To characterize IPF using Osc. Methods: IPF patients identified in accordance with current ATS/ETS guidelines, were prospectively enrolled, assessed with paired Osc-PFTs, six-minute walk test (6MWT) and Quality of Life questionnaire. Results: Sixty-five patients (21F, 44M; mean age=71.3±7.9) were studied from Sept 2019-Jan 2020. PFTs data revealed (mean±SD):%FVC=70.5±16.2, %FEV1=75.4±17.0, %TLC=69.9±12.6, %RV=72.3±14.8, %DCO=61.3±18.7, %6MWT=106.0±22.0. Osc measurements were: AX=15.9±10.4, X5=3.6±1.2, Fres=21.2±4.9. Fres correlated with %FVC and %TLC (AIC=381 and 310, r2=0.19 and 0.16), respectively. %RV/TLC correlated highly with X5 (AIC=143, r2=0.23) and AX (AIC=392, r2=0.19). %Distance walked on 6MWT was mostly highly correlated with %DCO and %FVC (AIC=441 and 554, r2=0.38 and 0.16), respectively. Conclusions: Our preliminary findings suggest that Osc may provide information regarding IPF disease severity and used as an adjunct to PFT, particularly those who cannot tolerate spirometry and/or body plethysmography.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".