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Record W2065700945 · doi:10.1001/archdermatol.2009.264

Alefacept for Severe Alopecia Areata

2009· article· en· W2065700945 on OpenAlexaff
Bruce Strober, Kavita Menon, Amy McMichael, Maria Hordinsky, Gerald G. Krueger, Jackie Panko, Kimberly Siu, Jonathan L. Lustgarten, Elizabeth K. Ross, Jerry Shapiro

Bibliographic record

VenueArchives of Dermatology · 2009
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineAlopecia areataDermatologyPlaceboScalpPsoriasisHair lossClinical trialFood and drug administrationSeverity of illnessRandomized controlled trialInternal medicinePharmacology

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the efficacy of alefacept for the treatment of severe alopecia areata (AA). DESIGN: Multicenter, double-blind, randomized, placebo-controlled clinical trial. SETTING: Academic departments of dermatology in the United States. PARTICIPANTS: Forty-five individuals with chronic and severe AA affecting 50% to 95% of the scalp hair and resistant to previous therapies. Intervention Alefacept, a US Food and Drug Administration-approved T-cell biologic inhibitor for the treatment of moderate to severe plaque psoriasis. Main Outcome Measure Improved Severity of Alopecia Tool (SALT) score over 24 weeks. RESULTS: Participants receiving alefacept for 12 consecutive weeks demonstrated no statistically significant improvement in AA when compared with a well-matched placebo-receiving group (P = .70). Conclusion Alefacept is ineffective for the treatment of severe AA.

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)0.001
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.010
GPT teacher head0.269
Teacher spread0.258 · 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

Citations50
Published2009
Admission routes1
Has abstractyes

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