Risk of Depression in Patients With Psoriatic Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Previous systematic reviews have assessed the prevalence and odds ratio (OR) of depression for patients with psoriatic disease. Due to probable bidirectional effects, prevalence and prevalence ORs are difficult to interpret. No prior reviews have quantified the relative risk (RR) of depression following a diagnosis of psoriatic disease. OBJECTIVE: To estimate the RR of depression in individuals with psoriasis and in psoriatic arthritis (PsA), clear-to-moderate psoriasis, and moderate-to-severe psoriasis subgroups. METHODS: Observational studies investigating the risk of depression in adults with psoriatic disease were systematically searched for in Medline, EMBASE, PsycINFO, and CINAHL databases; 4989 unique references were screened. Studies that reported measures of incident depression in psoriasis patients were included. Thirty-one studies were included into the systematic review, of which 17 were meta-analyzed. Random effects models were employed to synthesize relevant data. Sources of heterogeneity were explored with subgroup analysis and meta-regression. RESULTS: = 99.8%). Subgroup analysis and meta-regression did not indicate that PsA status or psoriasis severity (clear-to-mild, moderate-to-severe) were sources of heterogeneity. No evidence of publication bias was found. CONCLUSIONS: This review demonstrates that the risk of depression is greater in patients with psoriasis and PsA. Future research should focus on developing strategies to address the mental health needs of this patient population for depression, including primary prevention, earlier detection, and treatment strategies.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.003 | 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".