Temporal variations of oscillometric reactance in COPD and ILD
Bibliographic record
Abstract
Objective: Oscillometry (OS) measures lung function with minimal patient effort. Sugiyama et. al (Resp. Med. 2013, 107: 875-82) suggested that intra-breath variations in reactance at 5 Hz (X5) may discern COPD and ILD. We wished to study if intra-breath changes in X5 can further distinguish COPD, ILD and COPD-ILD overlap. Methods: OS measurements (tremoFlo AOS, THORASYS Inc.) were obtained from 40 subjects equally distributed into 5 groups: COPD with Emphysema confirmed by quantitative CT scan (CE); COPD without Emphysema (CX); COPD-ILD overlap (CI), ILD only (IL) and healthy controls (HC). We graphed X5 over volume (X5(V)) and calculated total, inspiratory and expiratory X5 (X5tot, X5in, X5ex, respectively), and X5in – X5ex (ΔX5). Results: Relative to HC, the CE group showed a strong downward shift and shape change in X5(V), and significant changes to all parameters (p<0.05, Holm–Bonferroni; Figure). CX and CI curves were altered to a lesser degree, and their parameters did not reach significance relative to HC or CE (p=0.08…0.14). For IL, we observed no changes in outcome parameters or X5(V). Discussion: Our data confirmed known changes of X5 in COPD but failed to reproduce Sugiyama9s increasingly negative ΔX5 in ILD. We also did not observe shape changes in X5(V) that might help detect ILD, with or without overlap with COPD. However, intra-breath changes in X5 may help distinguish emphysematous from non-emphysematous COPD.
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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 | 0.000 |
| 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) | 0.001 | 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".