Long-term non-invasive ventilation therapies in children: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Non-invasive ventilation (NIV) in children has become an increasingly common modality of breathing support where pressure support is delivered through a mask interface or less commonly through other non-invasive interfaces. At this time, NIV is considered a first-line option for ventilatory support of chronic respiratory insufficiency associated with a range of respiratory and sleep disorders. Previous reviews on the effectiveness, complications and adherence to NIV treatment have lacked systematic methods. The purpose of this scoping review is to provide an overview of the evidence for the use of long-term NIV in children. METHODS AND ANALYSIS: We will use previously established scoping methodology. Ten electronic databases will be searched to identify studies in children using NIV for longer than 3 months outside an intensive care setting. Grey literature search will include conference proceedings, thesis and dissertations, unpublished trials, reports from regulatory agencies and manufacturers. Two reviewers will independently screen titles and abstracts for inclusion, followed by full-text screening of potentially relevant articles to determine final inclusion. Data synthesis will be performed at three levels: (1) an analysis of the number, publication type, publication year, and country of publication of the studies; (2) a summary of the study designs, outcomes measures used; (3) a thematic analysis of included studies by subgroups. ETHICS AND DISSEMINATION: This study will provide a wide and rigorous overview of the evidence on the use of long-term NIV in children and provide critical information for healthcare professionals and policymakers to better care for this group of children. We will disseminate our findings through conference proceedings and publications, and evaluate the results for further systematic reviews and meta-analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.093 | 0.078 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.073 | 0.013 |
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".