Dry eye disease and psychiatric disorders: A systematic review and meta-analysis
Bibliographic record
Abstract
The association between dry eye disease (DED) and psychiatric conditions is a highly researched topic. This work reviews the literature on this relationship, examining the prevalence and correlations of depression and anxiety with dry eye signs and symptoms. A comprehensive literature search of MEDLINE, EMBASE, PsycINFO, and gray literature was conducted, with keywords for dry eye and mood disorders, depression, anxiety, and suicide. Eligible studies underwent quality assessment using the Newcastle-Ottawa Scale. Meta-analysis was performed using STATA 15.0. Fixed- and random-effects models were computed based on the presence of heterogeneity. Thirty-two studies were included, with 31 reporting on depression and 19 on anxiety. Meta-analysis results found a depression prevalence of 40% (CI: [0.29, 0.52]) in DED patients, with 1.81 times higher odds of prevalence compared to controls (CI: [1.61, 2.02]). Prevalence of anxiety was 39% (CI: [0.15, 0.64]), with 2.32 times higher odds of prevalence compared to controls (CI: [1.67, 3.23]). Depression scores were significantly higher in patients with DED in all studies. Anxiety scores were significantly higher in DED patients in studies using all scales except the Hospital Anxiety and Depression Scale-Anxiety Subscale. DED symptom scores were significantly associated with depression (ES = 0.43; CI: [0.31, 0.55]) and anxiety (ES = 0.41; CI: [0.32, 0.50]) scores. In conclusion, depression and anxiety are more prevalent and severe in DED patients and are correlated with dry eye symptoms but not signs. These findings highlight the interrelationship between these disorders and have important implications for providing appropriate care to these patients.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".