Prevalence of anxiety and depression symptoms in age-related macular degeneration patients: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Introduction: Age-related macular degeneration (AMD) is often associated with psychiatric conditions, notably anxiety and depression due to their poor visual function. Recognizing this relationship is important for healthcare professionals to holistically treat AMD patients potentially facing these psychiatric challenges. Aims: This study aims to determine the prevalence of anxiety and depression symptoms in AMD patients. Materials and Methods: A systematic search was conducted across MEDLINE, CINAHL, PsycInfo, and EMBASE. This review included English-language studies on anxiety or depression in AMD patients aged 18 years and above. Studies on other psychiatric disorders, review articles, or case reports were excluded. Gray literature from conference abstracts, theses, and dissertations was manually searched. The risk of bias was assessed using the Downs and Black checklist. Data extraction focused on the prevalence of depressive and anxiety symptoms, and the meta-analysis was performed using STATA 14.0. Results: Our search yielded 104 records, with 47 advancing to full-text review, resulting in the inclusion of 23 articles with 13,063 AMD patients. For depression, 18 studies were included, with significant heterogeneity between them ( I 2 = 99.2%, P < 0.001). A random effects model using the DerSimonian and Laird (D + L) method was applied, showing a pooled prevalence of depression of 22% (95% confidence interval [CI]: [15, 30]) among 12,444 AMD patients. Similarly, for anxiety, 17 studies were included, also showing a high level of heterogeneity ( I 2 = 96.9%, P < 0.001). Using the same D + L random effects model, the pooled prevalence of anxiety was 14% (95% CI: [10, 17]) among 12,021 AMD patients. Conclusions: Healthcare providers and patients should be aware of the high prevalence of anxiety (14%) and depression (22%) in AMD and consider the possibility of routine mental health screening and appropriate management.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".