Non-invasive Ventilation for Children With Chronic Lung Disease
Bibliographic record
Abstract
Advances in medical care and supportive care options have contributed to the survival of children with complex disorders, including children with chronic lung disease. By delivering a positive pressure or a volume during the patient's inspiration, NIV is able to reverse nocturnal alveolar hypoventilation in patients who experience hypoventilation during sleep, such as patients with chronic lung disease. Bronchopulmonary dysplasia (BPD) is a common complication of prematurity, and despite significant advances in neonatal care over recent decades its incidence has not diminished. Most affected infants have mild disease and require a short period of oxygen supplementation or respiratory support. However, severely affected infants can become dependent on positive pressure support for a prolonged period. In case of established severe BPD, respiratory support with non-invasive or invasive positive pressure ventilation is required. Patients with cystic fibrosis (CF) and advanced lung disease develop hypoxaemia and hypercapnia during sleep and hypoventilation during sleep usually predates daytime hypercapnia. Hypoxaemia and hypercapnia indicates poor prognosis and prompts referral for lung transplantation. The prevention of respiratory failure during sleep in CF may prolong survival. Long-term oxygen therapy has not been shown to improve survival in people with CF. A Cochrane review on the use NIV in CF concluded that NIV in combination with oxygen therapy improves gas exchange during sleep to a greater extent than oxygen therapy alone in people with moderate to severe CF lung disease. Uncontrolled, non-randomized studies suggest survival benefit with NIV in addition to being an effective bridge to transplantation. Complications of NIV relate mainly to prolonged use of a face or nasal mask which can lead to skin trauma, and neurodevelopmental delay by acting as a physical barrier to social interaction. Another associated risk is pulmonary aspiration caused by vomiting whilst wearing a face mask. Adherence to NIV is one of the major barriers to treatment in children. This article will review the current evidence for indications, adverse effects and long term follow up including adherence to NIV in children with chronic lung disease.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".