Detecting dynamic hyperinflation in COPD patients using a smart shirt: a pilot study
Bibliographic record
Abstract
Introduction: detection and quantification of dynamic hyperinflation (DH) in COPD patients in real-life is an intriguing issue, but hard to perform. This study assessed a smart shirt, capable of measuring breathing parameters, as a new diagnostic tool compared to standard lung function equipment. Methods: 31 healthy controls and 11 COPD patients performed basic spirometry, metronome-paced tachypnea (MPT) and an exercise test (either a six-minute-walk or a cardio-pulmonary exercise test (CPET)) wearing the smart shirt (Hexoskin, Carré Technologies, Montréal, Canada) and breathing through a sensor measuring flow. Breathing waveforms produced by the shirt were compared with mobile spirometry (Oxycon Mobile, Care Fusion, San Diego, USA) for its ability to register breathing (e.g. FEV1, IVC and breathing frequency) and detect DH. Results: Moderate-high correlations between the two devices were observed during basic spirometry (median Spearman’s rho for FEV1 (0.81), IVC (0.90) and frequency (0.95)) and during the longer MPT and exercise measurements (range 0.80 - 0.95). The agreement between the shirt and the mobile spirometry with respect to the detection of DH was poor to fair (kappa 0.2-0.3). The smart shirt detected DH more often than the spirometer. Conclusion: the smart shirt is capable of registering breathing patterns and alterations in breathing over time and DH can be detected. Several disturbing factors (such as movement, skin temperature and smart shirt sizing) need to be addressed to improve the detection algorithm. Disclosure: the authors declare no conflict of interest with any company whose equipment has been used.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".