Feasibility of a Respiratory Movement Evaluation Tool to Quantify Thoracoabdominal Movement for Neuromuscular Diseases
Bibliographic record
Abstract
BACKGROUND: An objective method to evaluate thoracoabdominal movement is needed in daily clinical practice to detect patients at risk of hypoventilation and to allow for timely interventions in neuromuscular diseases. The clinical feasibility, reliability, and validity of a newly developed method for quantifying respiratory movement using fiber grating sensors, called the Respiratory Movement Evaluation Tool (RMET), was evaluated. METHODS: The time needed to measure respiratory movement and the usability of the measurement were determined by 5 clinicians using the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) 2.0 questionnaire. Thoracoabdominal movement was measured using RMET 3 times in 10 healthy subjects to evaluate intraclass correlation coefficients (ICC). The subjects were encouraged to breathe 10 times while voluntarily changing the amount of air during ventilation simultaneously with the RMET and a spirometer, and their correlations were evaluated to test validity using Pearson's product-moment correlation coefficients. The same measurements were also performed in 10 subjects with Duchenne muscular dystrophy. RESULTS: Real-time recordings of thoracoabdominal movements were obtained over a mean time of 374 ± 23.9 s. With QUEST 2.0, the median score of each item exceeded 3 (more or less satisfied). In healthy subjects, ICC(1,1) ranged from 0.82 to 0.99, and ICC(2,1) ranged from 0.83 to 0.97. Significant correlations were observed between the respiratory amplitudes measured with RMET, and the amount of air during ventilation was measured with a spirometer (r = 0.995, P < .001). In subjects with Duchenne muscular dystrophy, ICC(1,1) ranged from 0.87 to 0.97, and ICC(2,1) ranged from 0.84 to 0.99. The respiratory amplitudes measured with RMET correlated significantly with the amount of air during ventilation with a spirometer (r = 0.957, P < .001). CONCLUSIONS: We developed a novel method of quantifying respiratory movement called RMET that was feasible to use in daily clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".