0886 LONG-TERM NON-INVASIVE VENTILATION THERAPIES IN CHILDREN: A SCOPING REVIEW
Bibliographic record
Abstract
Long-term non-invasive ventilation (NIV) is a common modality of breathing support used for a range of sleep and respiratory disorders. The aim of this scoping review was to provide a summary of the literature relevant to long-term NIV use in children. We used systematic methodology to identify 11581 studies with final inclusion of 289. The search was run in nine databases with additional grey literature sources. The search was limited to human studies published between 1990–2016. Inclusion criteria were: children 0–18 years; and NIV use greater than 3 months outside acute settings. Study design or outcomes assessed were not limited. We identified 76 terms referencing to NIV. Study design characteristics were most often single center (84%), observational (63%), and retrospective (54%). NIV use was reported for 73 medical conditions with obstructive sleep apnea (29%) and spinal muscular atrophy (8%) as the most common conditions. There were significant differences in medical conditions across ages (Pearson Chi-square 112.4, p<0.05). Continuous positive airway pressure (CPAP) was used in 25% of studies, versus 19% bilevel positive airway pressure, 2% auto-PAP, and 42% combination of CPAP and bilevel. Descriptive data, including NIV incidence (61%) and patient characteristics (51%), were most commonly reported. Outcomes from sleep studies were reported in 27% of studies followed by outcomes on respiratory morbidity such as improvement of respiratory symptoms, tracheostomy avoidance or decannulation, or reduction in post-operative complications in 15%. Reduction in other symptoms including sleep, neurocognition, mood, behavior and quality of life were reported in less than 5% of studies. Mortality was an outcome of interest in 6% of studies. Outcomes assessed differed by disease category (Pearson Chi-square 19.6, p<0.05). Adverse events and adherence were reported in 20% and 26% of articles respectively. Authors reported positive conclusions for 73% of studies. Long-term use of NIV has been documented in a large variety of pediatric patient groups with studies of lower methodological quality. Data was unevenly available across medical conditions. Stollery Clinical Research Fellowship funded by the Stollery Children’s Hospital Foundation.Women. Children’s Health Research Institute (WCHRI) through the Alberta Research Centre for Health.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".