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Systemic Therapies for Psoriatic Disease and Serious Infections in Older Adults

2025· letter· en· W4408591396 on OpenAlexaffabout
Aaron M. Drucker, Rinku Sutradhar, Vicki Ling, Jodi M. Gatley, Lihi Eder, Christine Fahim, Michael Fralick, Tara Gomes, Ping Li, Morris F. Manolson, Paula A. Rochon, Mina Tadrous

Bibliographic record

VenueJAMA Dermatology · 2025
Typeletter
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsSinai Health SystemInstitute for Clinical Evaluative SciencesPublic Health OntarioWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicinePsoriatic arthritisTofacitinibInternal medicineCohortPopulationDiseasePsoriasisCohort studyImmunologyRheumatoid arthritisEnvironmental health

Abstract

fetched live from OpenAlex

Importance: Systemic treatments for psoriatic disease affect the immune system and may increase infection risk. Older adults are at high risk for infection, and the relative safety of systemic treatments for them is unknown. Objective: To evaluate the association of systemic treatments for psoriatic disease with rates of serious infection among older adults. Design, Setting, and Participants: This cohort study used linked population-based health administrative data from 2002 to 2021 in Ontario, Canada. Participants included Ontario residents 66 years and older with psoriatic disease who were dispensed their first systemic medication between April 1, 2002, and December 31, 2020. Data were analyzed between November 2021 and August 2024. Exposure: Time-varying use of systemic medications categorized as (1) methotrexate; (2) other older systemic medications; (3) anti-tumor necrosis factor (anti-TNF) biologics; (4) other biologics (targeting interleukin [IL]-12, IL-23, and IL-17); and (5) tofacitinib. Main Outcomes and Measures: The main outcome was time to serious infection, defined as hospitalization for any infectious cause occurring up to March 2021. Multivariable Andersen-Gill recurrent event regression was used to estimate the association between each medication category and serious infection rates. The relative rates (RRs) of serious infection with 95% CIs for time actively using each medication category vs time not using that medication category were calculated. Results: Of 11 641 new users of systemic therapy, 6114 (53%) were female, and the median (IQR) age was 71 (68-76) years. There were 1967 serious infections during a median (IQR) of 4.8 (2.3-8.4) years of follow-up. There were 2.7 serious infections per 100 person-years using methotrexate, 2.5 per 100 person-years using other older systemic drugs, 2.2 per 100 person-years using anti-TNF biologics, 1.4 per 100 person-years using other biologics, and 8.9 per 100 person-years using tofacitinib. In the multivariable-adjusted model, methotrexate (RR, 0.95 [95% CI, 0.85-1.07]), other older systemic medications (RR, 0.92 [95% CI, 0.79-1.07]), and anti-TNF biologics (RR, 0.87 [95% CI, 0.69-1.10]) were not associated with serious infection compared to person-time not using those respective medications. Other biologics (RR, 0.65 [95% CI, 0.48-0.88]) were associated with lower rates of serious infection, whereas tofacitinib (RR, 2.89 [95% CI, 1.14-7.34]) was associated with higher rates of serious infection. Conclusions and Relevance: In this cohort study, biologics targeting IL-12, IL-23, or IL-17 were associated with a lower rate of serious infection among older adults with psoriatic disease. These biologics may have important safety benefits for older adults with higher infection 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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.006
GPT teacher head0.251
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations6
Published2025
Admission routes2
Has abstractyes

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