Does diaphragm fatigue affect inspiratory capacity during cardiopulmonary exercise testing?
Bibliographic record
Abstract
Inspiratory capacity (IC) measurements are used to evaluate inspiratory constraints during cardiopulmonary exercise testing. Diaphragm fatigue (DF) may impact IC accuracy. This study aimed to evaluate DF’s influence on IC in healthy humans. Fifteen males (27±5yrs, peakV̇O2=46±10mL/kg/min) with normal pulmonary and respiratory muscle function (PImax=115±25%pred) performed 2 constant load cycle tests with or without prior DF (induced with pressure-threshold loading) in random order on separate days. ICs were performed at rest and every 2-min during exercise. Esophageal pressure (Pes), crural diaphragm electromyography (EMGdi), and scalene EMG (EMGsca) were recorded. The interaction between condition and time was not significant for IC or associated Pes, EMGdi, and EMGsca (Fig1A-D). However, marginal contrasts showed lower IC from 0-60% exercise time (150-250mL; Fig1A), slightly lower Pes from 80-100% (3-4cmH2O; Fig1B), and higher EMGsca from 20-60% (1-2%max; Fig1D) with DF. IC did not differ significantly with DF across exercise in healthy males, likely due to compensation by other inspiratory muscles. While ICs performed soon after a highly fatiguing stimulus may be decreased, whether or not this difference is physiologically meaningful remains unclear, especially since there were no differences in IC as exercise progressed. Future work should assess DF’s impact in those with compromised respiratory muscle function. erj;66/suppl_69/OA5401/F1 F1 F1
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 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".