Global Burden of Anxiety and Depression among Cystic Fibrosis Patient: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
AIMS: This systemic review and meta-analysis were aimed at determining the level of anxiety and depression among cystic fibrosis patients in the world. METHODS: We conducted a systematic search of published studies from PubMed, EMBASE, MEDLINE, Cochrane, Scopus, Web of Science, CINAHL, and manually on Google Scholar. This meta-analysis follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The quality of studies was assessed by the modified Newcastle-Ottawa Scale (NOS). Meta-analysis was carried out using a random-effects method using the STATA™ Version 14 software. Trim and fill analysis was done to correct the presence of significant publication bias. RESULT: From 419,820 obtained studies, 26 studies from 2 different parts of the world including 9766. The overall global pooled prevalence of anxiety and depression after correction for publication bias by trim and fill analysis was found to be 24.91(95% CI: 20.8-28.9) for anxiety. The subgroup analyses revealed with the lowest prevalence, 23.59%, (95% CI: 8.08, 39.09)) in North America and the highest, 26.77%, (95% CI: 22.5, 31.04) seen in Europe for anxiety and with the highest prevalence, 18.67%, (95% CI: 9.82, 27.5) in North America and the lowest, 13.27%, (95% CI: -10.05, 16.5) seen in Europe for depression. CONCLUSION: The global prevalence of anxiety and depression among cystic fibrosis patients is common. Therefore, close monitoring of the patient, regularly screening for anxiety and depression, and appropriate prevention techniques is recommended.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.048 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".