Evolution of Diaphragm Thickness during Mechanical Ventilation. Impact of Inspiratory Effort
Bibliographic record
Abstract
RATIONALE: Diaphragm atrophy and dysfunction have been reported in humans during mechanical ventilation, but the prevalence, causes, and functional impact of changes in diaphragm thickness during routine mechanical ventilation for critically ill patients are unknown. OBJECTIVES: To describe the evolution of diaphragm thickness over time during mechanical ventilation, its impact on diaphragm function, and the influence of inspiratory effort on this phenomenon. METHODS: In three academic intensive care units, 107 patients were enrolled shortly after initiating ventilation along with 10 nonventilated intensive care unit patients (control subjects). Diaphragm thickness and contractile activity (quantified by the inspiratory thickening fraction) were measured daily by ultrasound. MEASUREMENTS AND MAIN RESULTS: Over the first week of ventilation, diaphragm thickness decreased by more than 10% in 47 (44%), was unchanged in 47 (44%), and increased by more than 10% in 13 (12%). Thickness did not vary over time following extubation or in nonventilated patients. Low diaphragm contractile activity was associated with rapid decreases in diaphragm thickness, whereas high contractile activity was associated with increases in diaphragm thickness (P = 0.002). Contractile activity decreased with increasing ventilator driving pressure (P = 0.01) and controlled ventilator modes (P = 0.02). Maximal thickening fraction (a measure of diaphragm function) was lower in patients with decreased or increased diaphragm thickness (n = 10) compared with patients with unchanged thickness (n = 10; P = 0.05 for comparison). CONCLUSIONS: Changes in diaphragm thickness are common during mechanical ventilation and may be associated with diaphragmatic weakness. Titrating ventilatory support to maintain normal levels of inspiratory effort may prevent changes in diaphragm configuration associated with mechanical ventilation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".