Noninvasive Respiratory Support for Pediatric Acute Respiratory Distress Syndrome: From the Second Pediatric Acute Lung Injury Consensus Conference
Bibliographic record
Abstract
OBJECTIVES: To develop evidence-based recommendations for the Second Pediatric Acute Lung Injury Consensus Conference (PALICC) regarding the effectiveness of noninvasive respiratory support for pediatric acute respiratory distress syndrome (PARDS). These include consideration of the timing and duration of noninvasive ventilation (NIV) and high-flow nasal cannula (HFNC), whether effectiveness varies by disease severity or by characteristics of treatment delivery, and best practices for the use of NIV. DATA SOURCES: MEDLINE (Ovid), Embase (Elsevier), and CINAHL Complete (EBSCOhost). STUDY SELECTION: Searches included all studies involving the use of NIV or HFNC in children with PARDS or hypoxemic respiratory failure. DATA EXTRACTION: Title/abstract review, full-text review, and data extraction using a standardized data extraction form. DATA SYNTHESIS: The Grading of Recommendations Assessment, Development, and Evaluation approach was used to identify and summarize evidence and develop recommendations. Out of 6,336 studies, we identified 187 for full-text review. Four clinical recommendations were generated, related to indications, timing and duration of NIV in patients with PARDS, predictors of NIV failure and need for intubation (signs and symptoms of worsening disease including pulse oximetry saturation/Fio2 ratio), and use of NIV in resource-limited settings. Six good practice statements were generated related to how and where to deliver NIV, the importance of trained experienced staff and monitoring, types of NIV interfaces, the use of sedation, and the potential complications of this therapy. One research statement was generated related to indications of HFNC in patients with PARDS. CONCLUSIONS: NIV is a widely used modality for the treatment of respiratory failure in children and may be beneficial in a subset of patients with PARDS. However, there needs to be close monitoring for worsening disease and NIV failure.
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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 | 0.123 | 0.200 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.018 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.011 | 0.009 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".