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 ( P di ). Although P di 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 P di , 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 (POST 1 , 10–15 min and POST 2 , 30–35 min) using synchronous recordings of P di and subcostal ultrasound traces of the right crural hemidiaphragm during anterolateral magnetic stimulation of the phrenic nerves and progressive CO 2 rebreathing. Fatigue was quantified as pre‐ to post‐loading changes in twitch P di , excursion velocity (excursion/time) and power ( P di × velocity). Both tasks resulted in significant reductions in twitch P di ( P < 0.05). There were no effects of MIV on ultrasound‐derived measures. In contrast, IRL elicited a significant reduction in twitch excursion at POST 1 (−16%; P = 0.034) and significant reductions in excursion velocity at POST 1 (−32%; P = 0.022) and POST 2 (−28%; P = 0.013). These reductions in excursion velocity, alongside the concurrent reductions in twitch P di , resulted in significant reductions in diaphragm power at POST 1 (−48%; P = 0.009) and POST 2 (−42%; P = 0.008). Neither task significantly altered the contractile responses to CO 2 . 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".