Depression and Anxiety in Patients With Irreversible Vision Loss: Meta-Analysis and Systematic Review
Bibliographic record
Abstract
ObjectiveApproximately 295 million individuals globally live with moderate to severe irreversible vision loss, primarily due to conditions such as glaucoma, diabetic retinopathy, and age-related macular degeneration (AMD). Vision impairment diminishes quality of life leading to higher rates of depression and anxiety. This study investigated the prevalence of anxiety and depression in patients with irreversible vision loss, with a comparative analysis across the conditions of AMD, diabetic retinopathy, and glaucoma.MethodsA comprehensive literature search was conducted in Medline, Embase, CINAHL, and Cochrane databases, supplemented by manual searches of conference literature.ResultsThe prevalence of depression in patients with irreversible vision loss was found to be 21% (95% CI: 0.17-0.26) among 76 561 patients, with variations based on the cause: 27% (95% CI: 0.19-0.35) in AMD, 48% (95% CI: 0.32-0.64) in diabetic retinopathy, and 23% (95% CI: 0.16-0.29) in glaucoma. Anxiety prevalence was 22% (95% CI: 0.15-0.30) among 25 616 patients.ConclusionThe high prevalence of depression and anxiety underscores the need for comprehensive healthcare approaches that incorporate mental health support, including vision rehabilitation, psychotherapy, pharmacological interventions, and lifestyle modifications. Future research should explore factors that protect against anxiety and depression, as well as address the long-term effects of vision loss treatments on mental health outcomes.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".