Drug-induced sleep endoscopy compared with systematic adenotonsillectomy in the management of obstructive sleep apnoea in children: a systematic review and meta-analysis protocol
Bibliographic record
Abstract
INTRODUCTION: Obstructive sleep apnoea affects up to 6% of children worldwide. Although current guidelines recommend systematic tonsillectomy and adenoidectomy, many children do not benefit from these interventions. Drug-induced sleep endoscopy (DISE) allows the dynamic evaluation of patients' airways to identify the specific anatomic sites of obstruction. This intervention can potentially guide subsequent invasive procedures to optimise outcomes and minimise the number of children exposed to unnecessary operations. METHODS AND ANALYSIS: We will identify randomised controlled trials and controlled observational studies comparing DISE-directed interventions to systematic tonsillectomy and adenoidectomy in paediatric populations. We will search MEDLINE, EMBASE, CINAHL, CENTRAL as well as clinical trial registries and conference proceedings (initial electronic search date 9 October 2018). Screening, data extraction and risk of bias assessments will be performed in duplicate by independent reviewers. We will use the Grading of Recommendations Assessment, Development and Evaluation approach to assess the overall quality of evidence and present our results. ETHICS AND DISSEMINATION: Ethics approval is not required for this systematic review of published data. This review will be presented according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We will present our findings at otorhinolaryngology conferences and publish a report in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42018085370.
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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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.026 | 0.027 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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".