Cohort study of malignancies and hospitalized infectious events in treated and untreated patients with psoriasis and a general population in the United States
Bibliographic record
Abstract
BACKGROUND: Psoriasis is associated with risk of malignancy. Some psoriasis treatments may increase the risk of hospitalized infectious events (HIEs). OBJECTIVES: To evaluate rates of malignancies and HIEs in patients with psoriasis. METHODS: This retrospective cohort study utilized data from MarketScan(®) databases. Cohorts included adult general population (GP), patients with psoriasis, and patients with psoriasis treated with nonbiologics, adalimumab, etanercept, infliximab or phototherapy. Outcomes included incidence rates (IRs) per 10 000 person-years observation (PYO) for all malignancies excluding nonmelanoma skin cancer (NMSC), lymphoma, NMSC, and per 10 000 person-years of exposure (PYE) for HIEs. RESULTS: Incidence rates [95% confidence interval (CI)] for all malignancies except NMSC were 129 (127-130) and 142 (135-149) for GP (PYO = 51 071 587) and psoriasis (PYO = 119 432) cohorts, respectively; 10·9 (10·5-11·3) and 12·9 (10·9-14·8) for lymphoma; and 145 (144-147) and 180 (173-188) for NMSC. Rates for all malignancies excluding NMSC were similar among treatments but variable for lymphoma and NMSC. IRs (95% CI) for HIEs were 332 (256-408) for the nonbiologic cohort (PYE = 3528); 288 (206-370) for etanercept (PYE = 6563); 325 (196-455) for adalimumab (PYE = 2772); 521 (278-765) for infliximab (PYE = 1058); and 334 (242-427) for phototherapy (PYE = 1797). IRs for HIEs were lowest for etanercept and higher in patients on baseline systemic corticosteroids across treatment cohorts. CONCLUSIONS: Malignancy rates were higher in patients with psoriasis than the GP, but these treatments did not appear to increase malignancy risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".