Characterization of disease burden, comorbidities, and treatment use in a large, US-based cohort: Results from the Corrona Psoriasis Registry
Bibliographic record
Abstract
BACKGROUND: Psoriasis is an immunodysregulatory inflammatory disease associated with comorbidities affecting quality of life. With the advent of new treatments, there is growing need to assess the long-term safety and efficacy of treatments in a real-world setting. OBJECTIVE: The objective of the Corrona Psoriasis Registry is to study the comparative safety and efficacy of Food and Drug Administration-approved biologic treatments. METHODS: A cross-sectional study of patients enrolled in the registry, who initiated or switched to a systemic therapy at enrollment or previous 12 months. Descriptive characteristics (demographics, clinical and patient-reported outcomes, comorbidities, and treatment history) were examined at registry enrollment. RESULTS: As of October 1, 2016, there were 1942 patients enrolled in the registry: 23% on apremilast, 4% on other nonbiologic systemic medications, 25% on interleukin (IL) 17A inhibitors, 22% on an IL-12/23 inhibitor, and 26% on tumor necrosis factor inhibitors. Overall, mean disease duration was 15.6 years, and 40% had a concurrent psoriatic arthritis diagnosis. About 66% had >3% body surface area involvement and 49% had a moderate or severe Investigator Global Assessment. LIMITATIONS: Selection and channeling bias can result in potential confounding that needs to be addressed in modeled analyses. CONCLUSION: This disease-based registry cohort represents a population exposed to multiple therapies, long disease duration, and multiple comorbidities and can be used to examine comparative safety and efficacy of various therapies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".