Oscillometry changes with body position and correlates with TLC and lung density
Bibliographic record
Abstract
Oscillometry (OS) is typically measured seated. Body position (BoP) influence on PFTs is well known but not on OS. We wished to determine BoP effect on OS and if PFTs or lung density (LD) would predict any changes. COPD and heathy subjects (HS) performed OS (tremoFlo C-100, 5-37Hz, Thorasys) sitting, supine and sitting again, and OS was determined; resistance at 5 Hz (R5), frequency dependence of resistance (R5-19), reactance at 5 Hz (X5), resonance frequency (Fres) and reactance area (AX). Absolute change (Δ) and %change (Δ%) sitting to supine OS was calculated. PFTs and CT scan LD (15th percentile density+1000HU, AirwayInspector.acil-bwh.org) were measured. Sitting vs. supine OS was compared with Holm9s corrected Student9s t-tests. Δ and Δ% were correlated with PFTs and LD by linear regression. R5Δ correlated with TLC (r=0.55, p=0.03, n=15), R5-19Δ with TLC (r=0.85, p<0.001, n=15) and LD (r=0.67, p=0.03, n=10), R5-19Δ% with RV/TLC (r=0.64, p=0.01, n=15) and AXΔ% with LD (r=0.62, p=0.05, n=10). OS changes with BoP in both COPD and HS. BoP changes in R5, R5-19, and AX correlate moderately to very strongly with TLC, RV/TLC or LD. These findings impact on the use of OS in supine patients and supine CT data to model airways for comparison with sitting OS, and suggest airway-parenchymal interdependence influences airway caliber. Independent confirmation is necessary before using R5-19Δ as a predictor of TLC or LD.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".