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Record W2024643527 · doi:10.1007/s10227-005-0117-0

Safety of Efalizumab in Patients with Moderate to Severe Chronic Plaque Psoriasis: Review of Clinical Data. Part II

2005· review· en· W2024643527 on OpenAlexaff
Kim Papp, Charles Camisa, Stephen Stone, Ivor Caro, Xiaolin Wang, Peter G. Compton, Patricia A. Walicke, Alice B. Gottlieb

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2005
Typereview
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsWestern University
FundersNational Cancer InstituteGenentech
KeywordsEfalizumabMedicineAdverse effectPlaceboPsoriasisClinical trialIncidence (geometry)DermatologyInternal medicinePlaque psoriasisPathologyAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The efficacy and safety of efalizumab have been evaluated in multiple clinical trials. OBJECTIVE: The purpose of this review is to provide an overview of the safety profile of efalizumab during the clinical trials. METHODS: Twelve-week data from four placebo-controlled trials were pooled and analyzed. Data from patients receiving 13-60 weeks of efalizumab therapy were pooled to evaluate longer-term safety. RESULTS: The most common adverse events were mild to moderate, self-limiting, flu-like symptoms that were most frequent following the first two efalizumab doses; by the third dose the incidence was comparable to placebo. Serious adverse events were observed in 2.2% and 1.7% of efalizumab- and placebo-treated patients, respectively. Nonserious adverse events leading to withdrawal were infrequent and similar to placebo (2.8% vs 1.8%). There does not appear to be increased risk of end-organ toxicity, infection, or malignancy in efalizumab-treated patients. CONCLUSION: Efalizumab was well tolerated, with a favorable safety profile.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.073
GPT teacher head0.343
Teacher spread0.271 · 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 designSystematic review
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

Citations23
Published2005
Admission routes1
Has abstractyes

Explore more

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