Oscillometry Parameters of a Patient Cohort Evaluated in a Tertiary Center
Bibliographic record
Abstract
Introduction: Small airway dysfunction (SAD) is the precursor of many respiratory diseases. Respiratory oscillometry is a simple, noninvasive method for evaluating the small airways. This study aims to determine the prevalence of SAD in respiratory patients treated at a tertiary ARIA/ISAR care center. Methods: This retrospective, single-center study used oscillometry to evaluate patients presenting with persistent cough and/or dyspnea. PulmoScan device was used following ERS guidelines. SAD was defined as R5–20> 0.7 cmH2O/L/s. The evaluated parameters included R5, R20, R5–20, X5, Fres and AX. Descriptive statistics were used to summarize the data. Results: We included 211 patients; 135 (63%) were female and the mean age was 46 (±25SD) years. 58 patients (27%) were diagnosed with asthma, 109 (52%) with post viral cough (PVC), 22 (10%) with allergic rhinitis (AR), 18 (9%) with COPD, and 4 (2%) with ILD. Total mean R5-20 was 0.96 (±0.88) and X5 was -2.59 (±1.72) cmH2O/L/s. SAD was found in 105 patients; in SAD+ patients mean R5-20 was 1.57 (±0.83) vs SAD- 0.33 (±0.30) cmH2O/L/s. Regarding SAD+ patients, 49% had PVC, 33% asthma, 11% COPD, 5% AR, and 2% ILD. Across different pathologies, SAD was more prevalent in COPD patients (66%, 12/18), followed by asthma (60%, 35/58), ILD (50%, 2/4), PVC (46%, 51/109), and AR (23%, 5/22). PVC patients also showed low X5 (-2.44) cmH2O/L/s. Those with SAD had lower values (-3.38 cmH2O/L/s) than those without SAD (-1.61 cmH2O/L/s). Conclusion: Oscillometry is a promising tool that can assist in the detection of SAD across a spectrum of respiratory disorders. It could potentially facilitate treatment of respiratory disorders and improve outcomes.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".