Predictors of longitudinal outcomes for children using long‐term noninvasive ventilation
Bibliographic record
Abstract
BACKGROUND: Noninvasive ventilation (NIV) is a first-line therapy for sleep-related breathing disorders and chronic respiratory insufficiency. Evidence about predictors that may impact long-term NIV outcomes, however, is scarce. The aim of this study is to determine demographic, clinical, and technology-related predictors of long-term NIV outcomes. METHODS: A 10-year multicentred retrospective review of children started on long-term continuous or bilevel positive airway pressure (CPAP or BPAP) in Alberta. Demographic, technology-related, and longitudinal clinical data were collected. Long-term outcomes examined included ongoing NIV use, discontinuation due to improvement in underlying conditions, switch to invasive mechanical ventilation (IMV) or death, patient/family therapy declination, transfer of services, and hospital admissions. RESULTS: A total of 622 children were included. Both younger age and CPAP use predicted higher likelihood for NIV discontinuation due to improvement in underlying conditions (p < .05 and p < .01). Children with upper airway disorders or bronchopulmonary dysplasia were less likely to continue NIV (p < .05), while presence of central nervous system disorders had a higher likelihood of hospitalizations (p < .01). The presence of obesity/metabolic syndrome and early NIV-associated complications predicted higher risk for NIV declination (p < .05). Children with more comorbidities or use of additional therapies required more hospitalizations (p < .05 and p < .01) and the latter also predicted higher risk for being switched to IMV or death (p < .001). CONCLUSIONS: Demographic, clinical data, and NIV type impact long-term NIV outcomes and need to be considered during initial discussions about therapy expectations with families. Knowledge of factors that may impact long-term NIV outcomes might help to better monitor at-risk patients and minimize adverse outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".