Prevalence and influencing factors of sleep disorders in patients with CRS: a protocol for systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis (CRS) is a common chronic disease that seriously affects patients' quality of life and imposes a heavy physical and mental burden on patients. There is growing evidence that sleep disorders are strongly associated with patients with CRS. However, there is no systematic evidence to clarify the prevalence and influencing factors of sleep disorders in patients with CRS with nasal polyps (NP) (CRSwNP) and CRS without NP (CRSsNP). For this reason, this study will systematically analyse the prevalence of sleep disorders in patients with CRSwNP and CRSsNP and explore the related influencing factors. METHODS AND ANALYSIS: We will electronically search PubMed, Web of Science, Embase, Cochrane, Ovid, Scopus, the China National Knowledge Infrastructure, the Wanfang database, the China Biomedical Literature Database and the China Scientific Journals Database from the establishment of the database to September 2023 to collect the prevalence of sleep disorders in patients with CRSwNP or CRSsNP and related studies on factors affecting sleep disorders. Two researchers will independently conduct literature screening and data extraction and evaluate the quality of the included studies using the Newcastle-Ottawa Quality Scale and Agency for Healthcare Research and Quality scales. The extracted data will be meta-analysed using Review Manager 5.3 and Stata 14.0 software, and the quality of the evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation. Publication bias will be assessed using the funnel plots, Egger's test and Begg's test. ETHICS AND DISSEMINATION: This review will not require ethical approval, as we will only use research data from the published documents. Our final findings will be published in a peer-reviewed, open-access journal for dissemination. PROSPERO REGISTRATION NUMBER: CRD42023446833.
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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 | 0.063 | 0.101 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.025 | 0.036 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 0.003 |
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".