O037 / #606: NON-INVASIVE VENTILATION PRACTICE FOR PRIMARY RESPIRATORY MANAGEMENT IN THE PEDIATRIC INTENSIVE CARE UNITS: AN INTERNATIONAL STUDY
Bibliographic record
Abstract
Aims & Objectives: To describe current practices and outcomes of Non-Invasive Ventilation (NIV) in PICUs internationally. Methods: IRB approved point prevalence study over 10 study weeks at 52 PICUs in 12 countries. Children were included if NIV was newly initiated on screening days, including high flow nasal cannula [HFNC: > 3LPM (Term / <1 year), > 4LPM (1-3 years), or >5LPM (>3 years)], continuous (CPAP) or bilevel positive airway pressure (BiPAP), or other NIV mode. We excluded post-operative cardiac surgery, post-extubation, and home NIV. The risk factor analysis used mixed effects logistic regression with stepwise addition/deletion (retention cutoff: p=0.15) Results: Of 15,310 patients screened, 695 met inclusion criteria, including 472 (68%) HFNC, 158 (23%) BiPAP, 62 (9%) CPAP, and 3 other modes. Median age was 15.4 months (IQR: 5.1-63.3). Main NIV indications were acute hypoxemia (51%) and respiratory distress (27%). NIV interface was nasal cannula 399 (58%), nasal mask/prongs 111 (16%), bucco-nasal mask 18 (3%), full-face mask 154 (22%), or other 3 (0.4%). NIV duration was 2.1 days (1-4). NIV failure (endotracheal intubation) occurred in 18.6% cases, 69.8% of them before 48h of NIV (Figure). After adjustment for severity of illness, risk factors for NIV failure included meeting at-risk criteria for Pediatric Acute Respiratory Distress Syndrome (p=0.02) and exposure to parenteral nutrition (p=<0.001). Factors predictive of NIV success were respiratory admission category (p=0.001) and exposure to enteral nutrition (p=0.001).Conclusions: NIV is frequently used in PICUs, with heterogenous practice. NIV failure occurs in 18.6% cases, most frequently within the first 48hrs
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".