Real World Studies of Psoriasis and Mental Illness in Newfoundland and Labrador
Bibliographic record
Abstract
Background Psoriasis is a chronic, immune-mediated inflammatory disease with an implied connection to psychiatric disorders. Objective This study aims to illustrate an association between psoriasis and psychiatric disorders using real world data gathered from the Newfoundland and Labrador population. Methods Data on 15,100 patients with psoriasis and 75,500 controls (1:5) was collected from the Newfoundland and Labrador Centre for Health Information’s Electronic Health Records. The cases and controls were matched for age, sex, and geography. Indicators for psychiatric disorders include diagnosis of mental illnesses from physician’s visits and hospitalization records (all coded for mental health using ICD-9 and ICD-10 codes). Results 9,991 (66.2%) cases were identified to have at least one visit with a diagnostic code for mental illness compared to 42,276 (56.0%), P < .0001 in the control group. The percentage of people coded for anxiety was 36.50% compared to 28.95%, P < .0001; depression was 37.04% compared to 30.19%, P < .0001; and adjustment disorder was 6.89% versus 5.48%, P < .0001, among those with and without psoriasis, respectively. The greatest risk for anxiety [OR 1.4 (1.20, 1.67)] and depression [OR 1.65 (1.36, 2.00)] among psoriasis patients was between the 0 to 20 age group. Women with psoriasis are more likely to have anxiety [OR 1.08 (1.03, 1.13)], depression [OR 1.04 (1.01, 1.09)] and adjustment disorder [OR 1.07 (0.98, 1.17)] compared to female controls. Conclusion Our result shows that patients with psoriasis have an increased prevalence of mental illness. Using real world data to carry out further investigations will better elucidate this association and provide an increased understanding of the association between psoriasis and mental disorders.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".