Inspiratory Muscle Training in the Intensive Care Unit: A New Perspective
Bibliographic record
Abstract
BACKGROUND: Prolonged use of mechanical ventilation (MV) leads to weakening of the respiratory muscles, especially in patients subjected to sedation, but this effect seems to be preventable or more quickly reversible using respiratory muscle training. The aims of the study were to assess variations in respiratory and hemodinamic parameters with electronic inspiratory muscle training (EIMT) in tracheostomized patients requiring MV and to compare these variations with those in a group of patients subjected to an intermittent nebulization program (INP). METHODS: This was a pilot, prospective, randomized study of tracheostomized patients requiring MV in one intensive care unit (ICU). Twenty-one patients were randomized: 11 into the INP group and 10 into the EIMT group. Two patients were excluded in experimental group because of hemodynamic instability. RESULTS: In the EIMT group, maximal inspiratory pressure (MIP) after training was significantly higher than that before (P = 0.017), there were no hemodynamic changes, and the total weaning time was shorter than in the INP group (P = 0.0192). CONCLUSION: The EIMT device is safe, promotes an increase in MIP, and leads to a shorter ventilator weaning time than that seen in patients treated using INP.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".