Monitoring of Respiratory Muscle Function in Critically Ill Children
Bibliographic record
Abstract
OBJECTIVES: This review discusses the different techniques used at the bedside to assess respiratory muscle function in critically ill children and their clinical applications. DATA SOURCES: A scoping review of the medical literature on respiratory muscle function assessment in critically ill children was conducted using the PubMed search engine. STUDY SELECTION: We included all scientific, peer-reviewed studies about respiratory muscle function assessment in critically ill children, as well as some key adult studies. DATA EXTRACTION: Data extracted included findings or comments about techniques used to assess respiratory muscle function. DATA SYNTHESIS: Various promising physiologic techniques are available to assess respiratory muscle function at the bedside of critically ill children throughout the disease process. During the acute phase, this assessment allows a better understanding of the pathophysiological mechanisms of the disease and an optimization of the ventilatory support to increase its effectiveness and limit its potential complications. During the weaning process, these physiologic techniques may help predict extubation success and therefore optimize ventilator weaning. CONCLUSIONS: Physiologic techniques are useful to precisely assess respiratory muscle function and to individualize and optimize the management of mechanical ventilation in children. Among all the available techniques, the measurements of esophageal pressure and electrical activity of the diaphragm appear particularly helpful in the era of individualized ventilatory management.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".