Depressive symptoms, depression, and the effect of biologic therapy among patients in Psoriasis Longitudinal Assessment and Registry (PSOLAR)
Bibliographic record
Abstract
BACKGROUND: Patients with psoriasis are at an increased risk for depression. However, the impact of treatment on this risk is unclear. OBJECTIVE: Evaluate the incidence and impact of treatment on depression among patients with moderate-to-severe psoriasis. METHODS: We defined a study population within the Psoriasis Longitudinal Assessment and Registry and measured the incidence of depressive symptoms (Hospital Anxiety and Depression Scale-Depression score ≥8) and adverse events (AEs) of depression within cohorts receiving biologics, conventional systemic therapies, or phototherapy. Patients were evaluated at approximately 6-month intervals. Multivariate modeling determined the impact of treatment on risk. RESULTS: The incidence rates of depressive symptoms were 3.01 per 100 patient-years (PYs) (95% confidence interval [CI], 2.73-3.32), 5.85 per 100 PYs (95% CI, 4.29-7.97), and 5.70 per 100 PYs (95% CI, 4.58-7.10) for biologics, phototherapy, and conventional therapy, respectively. Compared with conventional therapy, biologics reduced the risk for depressive symptoms (hazard ratio, 0.76; 95% CI, 0.59-0.98), whereas phototherapy did not (hazard ratio, 1.05; 95% CI, 0.71-1.54). The incidence rates for AEs of depression were 0.21 per 100 PYs (95% CI, 0.15-0.31) for biologics, 0.55 per 100 PYs (95% CI, 0.21-1.47) for phototherapy, and 0.14 per 100 PYs (95% CI, 0.03-0.55) for conventional therapy; the fact that there were too few events (37 AEs) precluded modeling. LIMITATIONS: Incomplete capture of depression and confounders in the patients on registry. CONCLUSION: Compared with conventional therapy, biologics appear to be associated with a lower incidence of depressive symptoms among patients with psoriasis.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".