0938 A SYSTEMATIC REVIEW OF ADHERENCE TO LONG-TERM NON-INVASIVE VENTILATION IN CHILDREN
Bibliographic record
Abstract
Adherence to non-invasive ventilation (NIV) has been shown to optimize both day and nighttime gas exchange in children with documented physiological advantages. Minimal hours of treatment required for optimal effect have not been established. Variability in adherence to NIV in children also requires further examination. In this systematic review we summarized the available data on adherence and factors that influence adherence in children using NIV This extension of a scoping review on long-term NIV therapies in children identified all publications examining NIV adherence in children; 289 included publications were reviewed to identify those reporting on adherence. Grey literature sources and articles reporting only adherence rates were excluded. Data extraction on study design, sample size, intervention type, adherence measurements, barriers to adherence, and determinants of adherence will be completed Seventy five manuscripts mentioning adherence were identified from the scoping review of which 27 studies (1138 subjects) were included for data extraction. Objective measures of adherence were available in 21 (78%) of the studies. Preliminary analysis showed both patient and technology influences contributing to the variable rates of children’s adherence (e.g. patient age, interface type). Six studies reported on adherence measures, most commonly (70%) defined as an average of 4 or more hours of NIV use per night at least 5 nights a week. Only one study related adherence to outcome. This study reported longer duration of CPAP use correlated inversely with Epworth Sleepiness Scale scores This systematic review revealed gaps in the evidence on objective measures used to assess adherence in long term NIV use in children. The relationship between these measures and clinical outcomes in children was also limited. Identification of factors influencing NIV use in children requires further study in order to understand how to better support children to use long term NIV None
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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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".