Predictors of Longitudinal Outcomes for Children Using Long-term Non-invasive Ventilation
Bibliographic record
Abstract
Background: Non-invasive 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 ten-year multi-centred retrospective review of children started on long-term continuous or bilevel positive airway pressure (CPAP, BPAP) in Alberta. Demographic, technology-related, and longitudinal clinical data was collected. Long-term outcomes examined included ongoing NIV use, discontinuation due to improvement in underlying condition, switch to invasive mechanical ventilation (IMV) or death, patient/family therapy declination, transfer of services, and hospital admissions. Results: 622 children were included. Both younger age and CPAP use predicted higher likelihood for NIV discontinuation due to improvement in underlying conditions. Children with upper airway disorders or bronchopulmonary dysplasia were less likely to require NIV continuation while presence of central nervous system (CNS) disorders resulted in higher likelihood of hospitalizations and switch to IMV or death. The presence of obesity/metabolic syndrome and early NIV-associated complications predicted higher risk for NIV declination. Children with more co-morbidities or use of additional therapies required more hospitalizations and the latter also predicted higher risk to be switched to IMV or death. Conclusions: Demographic, clinical data, and NIV type impact long-term NIV outcomes and need to be considered during the 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 | 0.004 |
| 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 | 0.000 | 0.001 |
| 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".