Comparison of spirometry and both spectral and intra-breath oscillometry for the prediction COPD symptoms
Bibliographic record
Abstract
Introduction: Whole-breath oscillometry (OSC) is an effort-independent lung function test obtainable when spirometry is not. Intra-breath OSC (OSCIB), correlates better with symptoms than OSC or spirometry in adult asthma and IPF. The ability of OSCIB to predict COPD symptoms is unknown. Hypothesis: OSC and OSCIB correlate the COPD Assessment Test (CAT) and the mMRC as strongly as spirometry. Methods: 158 subjects with CAT, mMRC, biometrics, spirometry, OSC and OSCIB were included and underwent 3-5 measurements of OSC (tremoFlo C-100) yielding resistance (R) and reactance (X) parameters and then a 30-45s OSCIB measurement at 10 Hz yielding 10 Hz X from which novel X vs flow and volume parameters including the end-inspiratory X (XeI) were calculated. Groups included 1. COPD (≥10 pack-year, post-bronchodilator FEV1/FVC<0.70), 2. preCOPD (COPD, but post-bronchodilator FEV1/FVC≥0.70 and MMEF<65%predicted) and 3. Healthy. Kruskal-Wallis and post-hoc Dunn tests or χ2 tested between group differences, and Bonferroni-corrected Spearman rho, correlation strengths. Results: Correlations strengths are shown below for 104 COPD, 31 preCOPD and 23 Healthy. Conclusion: Spirometry, OSC and OSCIB all demonstrated moderate correlations with CAT and mMRC. Of all parameters, the novel XeI showed the strongest correlation with symptoms. Both OSC and OSCIB may be viable alternatives to spirometry for assessing COPD symptoms.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".