Ultrasonographic evaluation of diaphragm fatigue in healthy humans
Bibliographic record
Abstract
Abstract Assessment of diaphragm function and fatigue typically relies on the measurement of transdiaphragmatic pressure (Pdi). Although Pdi serves as an index of diaphragm force output, it provides limited information regarding the ability of the muscle to shorten and generate power. We asked whether ultrasonography, combined with Pdi, could be used to quantify changes in diaphragm function attributable to fatigue. Eight healthy men [mean (SD) age, 23 (7) years] completed two tasks on separate occasions: (i) 2 min of maximal isocapnic ventilation (MIV); or (ii) 3 × 5 min of maximal inspiratory resistive loading (IRL). Diaphragm function was evaluated before (PRE) and after each task (POST1, 10–15 min and POST2, 30–35 min) using synchronous recordings of Pdi and subcostal ultrasound traces of the right crural hemidiaphragm during anterolateral magnetic stimulation of the phrenic nerves and progressive CO2 rebreathing. Fatigue was quantified as pre‐ to post‐loading changes in twitch Pdi, excursion velocity (excursion/time) and power (Pdi × velocity). Both tasks resulted in significant reductions in twitch Pdi (P < 0.05). There were no effects of MIV on ultrasound‐derived measures. In contrast, IRL elicited a significant reduction in twitch excursion at POST1 (−16%; P = 0.034) and significant reductions in excursion velocity at POST1 (−32%; P = 0.022) and POST2 (−28%; P = 0.013). These reductions in excursion velocity, alongside the concurrent reductions in twitch Pdi, resulted in significant reductions in diaphragm power at POST1 (−48%; P = 0.009) and POST2 (−42%; P = 0.008). Neither task significantly altered the contractile responses to CO2. In conclusion, subcostal ultrasonography coupled with phrenic nerve stimulation is a promising method for quantifying contractile fatigue of the human 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 | 0.000 | 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) | 0.001 | 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".