Drop in oscillometry measures of reactance correspond to drop in forced vital capacity in IPF
Bibliographic record
Abstract
Background: Disease progression in idiopathic pulmonary fibrosis (IPF) is monitored by changes in forced vital capacity (FVC), diffusing capacity and 6-minute walk test (6MWT). Drops of 5% and 10% in FVC within 1 year is considered to be significant deterioration. Respiratory oscillometry is another pulmonary function test (PFT) conducted during tidal breathing. It's metrics of reactance in inspiration (X5.in) and at end-inspiration (XeI) are highly correlated with IPF severity, GAP score and CT derived PVV. Objective: To evaluate whether changes in X5.in and XeI correspond to 5% and 10% decline in FVC in IPF patients. Methods: From Sept 2019 to Jul 2024, 316 IPF patients were enrolled for paired spectral and monofrequency (10 Hz) oscillometry before each clinically-indicated routine PFTs and 6MWT. Results: Of the 316 patients enrolled, 176 (124M/52F; mean±SD age=74.5±8.3yrs) exhibited a FVC decline of 5% from baseline measurements. We observed significant worsening in X5.in (median = -2.1 vs -2.2 cmH₂O·s/L) and XeI (-0.5 vs. -0.7 cmH₂O·s/L), p<0.001 for both. In the 136 patients with FVC decline of ≥10%, X5.in and XeI were statistically significant (-2.1 vs -2.5 and -0.6 vs -0.8 cmH₂O·s/L, respectively, p<0.001 for both). Our data reveals that in patients with 5% FVC drop, corresponding drop in X5.in was -0.07 and XeI was -0.13 cmH₂O·s/L. For 136 patients with 10% FVC drop, X5.in and XeI dropped by -0.51 and 0.22 cmH₂O·s/L, respectively. Conclusion: Our finding suggests that the oscillometry metrics of X5.in and XeI could be used to monitor disease progression. As oscillometry is both easier to perform and well tolerated by patients, intergration into clinical practice should be considered.
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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 | 0.000 | 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) | 0.006 | 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".