Does non-invasive ventilation change metabolic markers in children with obstructive sleep apnoea? A systematic review and meta-analysis study protocol
Bibliographic record
Abstract
INTRODUCTION: Obstructive sleep apnoea (OSA) is not only common within paediatrics but is associated with critical childhood metabolic morbidity such as obesity, cardiovascular disease and glucose tolerance impairment. Increasing evidence suggests an association between childhood OSA and metabolic syndrome such as markers of cardiovascular disease, systemic hypertension, glucose intoleranceand increased lipid profile. Recent studies have targeted changes in metabolic markers in children using non-invasive ventilation (NIV) but no systematic reviews are available to summarise this emerging evidence. The purpose of this systematic review is to provide systematic synthesis of the evidence on the effect of NIV use on metabolic markers in children with OSA. METHODS AND ANALYSIS: A systematic search of electronic databases and grey literature will include paediatric interventional studies (random controlled trials, cohort studies) with and without a comparison group. Two reviewers will independently undertake the two step process of title/abstract and full-text screening. Data will be extracted and assessed, with aggregate data being reported. When the data allow, meta-analysis will be performed. ETHICS AND DISSEMINATION: There are no ethical concerns with this systematic review, as data have previously been published. This review will inform clinicians taking care of children with OSA and obesity/metabolic syndrome about the potential effects of NIV therapies on metabolic markers and has the potential to change the approach to childhood OSA and obesity. Results of this systematic review will be submitted for dissemination in abstract and manuscript form.
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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 | 0.057 | 0.080 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.024 | 0.023 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.006 |
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".