Evaluating the relationship between respiratory muscle electromyography and ultrasound in healthy subjects
Bibliographic record
Abstract
Introduction: Respiratory muscle function may be evaluated using non-invasive tests, including parasternal or diaphragm surface electromyography (EMGpara, EMGdi) and thoracic ultrasound (US). The relationship between respiratory muscle EMG and US indices has not previously been evaluated. Methods: Healthy subjects performed spirometry, sniff, maximal inspiratory and expiratory mouth pressures (SNIP, MIP, MEP). Tidal and sniff USdi excursion and end-tidal inspiratory parasternal thickness were assessed. EMGpara and US-guided EMGdi were performed to measure neural respiratory drive index (NRDI). Data are reported as mean (±SD) or median (IQR). Pearson or Spearman correlation analyses were performed as appropriate. Results: Twenty subjects (12 females, mean age 33.45±8.97 years) had: FEV1 99±12%pred, FVC 102±13%pred, SNIP 89±30 cmH2O, MIP 86±35 cmH2O, MEP 102±47 cmH2O. NRDIpara was 113 (59-251)%·bpm and NRDIdi was 90 (40-184)%·bpm. Sniff USdi excursion was 2.48 (1.90-2.90) cm, tidal USdi was 1.97 (1.47-2.10) cm. Right and left parasternal thickness were 0.36±0.15 and 0.33±0.14 cm, respectively. Inverse correlations were found between NRDIpara and sniff USdi excursion (r=-0.58, p=0.01) and SNIP (r=-0.69, p=0.001), and NRDIdi and sniff USdi (r=-0.45, p=0.04) and SNIP (r=-0.46, p=0.04). SNIP was positively correlated to sniff USdi excursion (r=0.57, p=0.01). Conclusion: In healthy subjects, surface electromyography of parasternal intercostal muscles and diaphragm is related to US-measured diaphragm excursion. Evaluation of these non-invasive tests of respiratory muscle function in disease states where there is load capacity imbalance of the respiratory muscle pump is warranted.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".