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Record W4293216296 · doi:10.1002/jmv.28085

Clinical characteristics and outcomes of patients with COVID‐19 and psoriasis

2022· review· en· W4293216296 on OpenAlexaff
Yu Meng, Furong Zeng, Huiyan Sun, Yayun Li, Xiang Chen, Guangtong Deng

Bibliographic record

VenueJournal of Medical Virology · 2022
Typereview
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsSKiN Health
FundersNational Natural Science Foundation of China
KeywordsMedicinePsoriasisInternal medicineConfidence intervalCoronavirus disease 2019 (COVID-19)Case fatality rateSystemic therapyRelative riskDiseaseEpidemiologyImmunologyInfectious disease (medical specialty)Cancer

Abstract

fetched live from OpenAlex

To summarize the clinical characteristics and explore the role of treatment types in outcomes among psoriasis patients with coronavirus disease 2019 (COVID-19). The principal summary measures were pooled prevalence and risk ratio (RR) with 95% confidential interval (CI). R statistic software was used for all the analysis. A total of 19 studies including 4073 psoriasis patients with COVID-19 were eligible for the meta-analysis. The overall hospitalization rate is about 20.2% (95% CI: 12.7%-28.7%), and changed to be 18.0% (95% CI: 9.9%-27.6%) or 14.1% (95% CI: 5.9%-24.6%) after systemic or biologic treatment. Moreover, the overall fatality rate is 1.5% (95% CI: 0.4%-3.0%), and turned to be 0.7% (95% CI: 0%-2.0%) or 0.5% (95% CI: 0%-2.2%) after systemic or biologic therapy. Notably, a lower hospitalization RR was found in patients receiving biologic therapy than those receiving other treatments (RR = 0.62, 95% CI: 0.42-0.94). The results were consistent after sensitivity analysis and trim-and-fill analysis. Systemic, especially biologic therapy could lessen the clinical severity in psoriasis patients with COVID-19. Our finding will help to guide current recommendations and provide a reference for clinical decision-making.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.934
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.052
GPT teacher head0.352
Teacher spread0.299 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

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