Measuring lung mechanics in patients with <scp>COPD</scp> using the handheld portable rapid expiratory occlusion monitor (<scp>REOM</scp>): A cross‐sectional study
Bibliographic record
Abstract
Abstract While conventional spirometry is associated with strenuous “forced” maximal respiratory maneuvers and infection control implications, oscillometry is not associated with these issues. However, portability, convenience of use, and interpretation remain common limitations to both techniques. This study tested the concordance and agreement between resistance measurements obtained from the handheld portable REOM device (R eo‐f , R eo‐s ) with those from conventional oscillometry (R 19 , R 5 ) in PFT‐confirmed “mild” (GOLD 1) and “very severe” (GOLD 4) COPD. Unadjusted and adjusted concordance (Spearman correlation) and agreement (Bland–Altman tests) served as co‐primary outcomes. Discrimination between GOLD 1 and 4 COPD (Wilcoxon rank sum test, Support Vector Machine (SVM) classifier) and patient user experience (System Utility Scale (SUS), Participant Satisfaction Survey (PSS)) served as secondary outcomes. In 17 participants (GOLD 1 n = 9, GOLD 4 n = 8), adjusted R 5 ‐R eo‐s (0.95 [0.81, 0.98]) and R 19 ‐R eo‐f (0.93 [0.79, 0.99]) correlations were very strong, as was agreement (mean differences: −0.07, 0.08, respectively). Statistically significant between‐group differences were observed for all four resistance parameters. R eo‐s in particular exhibited perfect discrimination between GOLD 1 and 4 disease, with some minor misclassification by R eo‐f , R 5 ( n = 1 each) and R 19 ( n = 4). User experience scores were excellent. These results support the capacity for REOM as a novel, complementary diagnostic device in COPD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".