Predictors of NIV‐related adverse events in children using long‐term noninvasive ventilation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: An increasing number of children with diverse medical conditions are using long-term noninvasive ventilation (NIV). This study examined the impact of demographic, clinical, and technology-related factors on long-term NIV adverse events in a large cohort of children using long-term NIV. METHODS: This was a multicenter retrospective review of all children who initiated long-term NIV in the province of Alberta, Canada, from January 2005 to September 2014, and followed until December 2015. Inclusion criteria were children who had used NIV for 3 months or more and had at least one follow-up visit with the NIV programs. RESULTS: We identified 507 children who initiated NIV at a median age of 7.5 (interquartile range: 8.6) years and 93% of them reported at least one NIV-related adverse event during the initial follow-up visit. Skin injury (20%) and unintentional air leaks (19%) were reported more frequently at the initial visit. Gastrointestinal symptoms, midface hypoplasia, increased drooling, aspiration and pneumothorax were rarely reported (<5%). Younger age and underlying conditions such as Down syndrome, achondroplasia, and Duchenne muscular dystrophy were early predictors of unintentional air leak. Younger age also predicted child sleep disruption in the short term and ongoing parental sleep disruption. Obesity was a risk factor for persistent nasal symptoms. Mask type was not a significant predictor for NIV-related short- or long-term complications. CONCLUSIONS: This study demonstrates that NIV-related complications are frequent. Appropriate mask-fitting and headgear adaptation, and a proactive approach to early detection may help to reduce adverse events.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".