Monitoring of Children With Pediatric Acute Respiratory Distress Syndrome
Bibliographic record
Abstract
OBJECTIVE: To critically review the potential role of monitoring technologies in the management of pediatric acute respiratory distress syndrome, and specifically regarding monitoring of the general condition, respiratory system mechanics, severity scoring parameters, imaging, hemodynamic status, and specific weaning considerations. DESIGN: Consensus conference of experts in pediatric acute lung injury. METHODS: A panel of 27 experts met over the course of 2 years to develop a taxonomy to define pediatric acute respiratory distress syndrome and to make recommendations regarding treatment and research priorities. The monitoring subgroup comprised two experts. When published data were lacking a modified Delphi approach, emphasizing strong professional agreement was used. RESULTS: The Pediatric Acute Lung Injury Consensus Conference experts developed and voted on a total of 151 recommendations addressing the topics related to pediatric acute respiratory distress syndrome, 21 of which related to monitoring of a child with pediatric acute respiratory distress syndrome. All 21 recommendations had agreement, with 19 (90%) reaching strong agreement. CONCLUSIONS: The Consensus Conference developed pediatric-specific recommendations related to monitoring children with pediatric acute respiratory distress syndrome. These include interpreting monitored values such as tidal volume using predicted body weight, monitoring tidal volume at the end of the endotracheal tube in small children, and continuous monitoring of exhaled carbon dioxide in intubated children with pediatric acute respiratory distress syndrome, among others. These recommendations for monitoring in pediatric acute respiratory distress syndrome are intended to promote optimization and consistency of care for children with pediatric acute respiratory distress syndrome and identify areas of uncertainty requiring further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".