Reliability of diaphragm voluntary activation measurements in healthy adults
Bibliographic record
Abstract
Voluntary activation can be used to assess central fatigue of the diaphragm after tasks such as exercise or inspiratory muscle loading. Cervical magnetic stimulation (CMS) of the phrenic nerves elicits an involuntary contraction, or twitch, of the diaphragm. This twitch is quantified based on a measure of transdiaphragmatic pressure and can be used to evaluate diaphragm contractile function and diaphragm voluntary activation (diaphragm-VA). The test–retest reliability of diaphragm-VA using CMS is currently unknown. Thirteen participants (4 male, 9 female; aged 25 ± 3 years) performed a series of interpolated twitch manoeuvres, which included a maximal inspiratory effort against a semi-occluded mouthpiece and 2 CMS-stimuli, 1 during the inspiratory manoeuvre and 1 after when the participant returned to functional residual capacity to quantify diaphragm-VA. Intraclass correlation coefficients (ICCs) and standard error of measurement (SEM) measured between-day and within-session reliability of diaphragm-VA, respectively. Maximal diaphragm-VA values were 91% (SD: 6; SEM: 3.9) and 92% (SD: 5; SEM: 2.2) during visits 1 and 2 (p = 0.68), respectively, and displayed “good” between-day reliability (ICC: 0.88; 95% confidence interval: 0.67–0.95; SEM: 2.7). Our results suggest that assessing diaphragm-VA using CMS is reliable in young healthy adults. Measuring diaphragm-VA may provide additional insight into the consequences and mechanisms of diaphragm fatigue. Novelty: Magnetic stimulation of the phrenic nerves can reliably measure voluntary activation of the diaphragm. Diaphragm voluntary activation can be used to provide additional insight into fatigability of the diaphragm.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".