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Record W1992622835 · doi:10.2310/7750.2008.07069

Efficacy of a Day-Care Program in the Treatment of Psoriasis

2008· article· en· W1992622835 on OpenAlexfundno aff
Junling Zhang, David N. Adam, Elaine Stebbing, Judith Gerbrandt, Harvey Lui, Jerry Shapiro, Youwen Zhou

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2008
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsMedicinePsoriasisPsoriasis Area and Severity IndexAdverse effectRetrospective cohort studyPlaque psoriasisPopulationDermatologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Few data exist documenting the effectiveness of psoriasis day-care treatment programs (PDTPs) using standardized efficacy measurements. OBJECTIVES: We sought to analyze the efficacy of a PDTP using the Psoriasis Area and Severity Index (PASI). METHODS: A retrospective review was performed on 132 patients treated at our PDTP. Sufficient data existed to permit PASI analysis using a simplified method for a representative subgroup of 64 patients, who formed the study population. Patients received phototherapy and topical treatments over 2 weeks. The outcome measures included a baseline and day 11 PASI, a physician global assessment (PGA), and adverse events reported by the patients. RESULTS: Mean baseline PASI was 13.6 (N = 64), with a 59.6% reduction by day 11. A PASI reduction of > or = 50% was seen in 75% of patients, with 30% of patients achieving > or = 75% reduction of PASI. Day 11 PGA demonstrated a 69.9% improvement. CONCLUSION: With a reduction in PASI of 59.6% at 11 days, our PDTP, with phototherapy and topical agents, seems to be a rapid and effective therapy for psoriasis.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.274
Teacher spread0.239 · 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 designNon-randomized trial
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".

Quick stats

Citations1
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cutaneous Medicine and Surgery→Same topicPsoriasis: Treatment and Pathogenesis→French-language works237,207→