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Record W2324184100 · doi:10.2310/7750.2007.00033

Efficacy of Imiquimod as an Adjunct to Cryotherapy for Actinic Keratoses

2007· article· en· W2324184100 on OpenAlexaff
Jerry Tan, David R. Thomas, Yves Poulin, Frances Maddin, Jing Tang

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2007
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsCentre de Recherche Dermatologique du Québec MétropolitainUniversité LavalWindsor Clinical ResearchUniversity of British Columbia
Fundersnot available
KeywordsMedicineCryotherapyImiquimodActinic keratosesDermatologyPhotodermatosisSurgeryPathologyBasal cell

Abstract

fetched live from OpenAlex

BACKGROUND: Cryotherapy is the standard of care for clinically apparent (target) actinic keratoses (AKs). Topical imiquimod may reduce initially inapparent or subclinical AKs. OBJECTIVE: We evaluated the potential of topical imiquimod to decrease subclinical AKs after cryotherapy of target AKs. METHODS: A randomized trial of imiquimod or vehicle twice weekly for 8 weeks following 3- to 5-second cryotherapy of target AKs within a 50 cm(2) field at the face or scalp was conducted. Efficacy outcomes included clearance of target, subclinical, and total AKs and proportions clear of AKs. Subjects with residual AKs were offered cryotherapy and open-label imiquimod twice weekly for 8 weeks. RESULTS: Sixty-three subjects completed the randomized phase. At 12 weeks, target AK clearance was similar for imiquimod and vehicle (79% vs 76%), but fewer total AKs were noted for imiquimod (78 vs 116). This was due to a progressive reduction in subclinical AKs with imiquimod compared with a progressive increase with vehicle. More subjects treated with imiquimod achieved clearance of subclinical (58% vs 34%; p = .06) and total (23% vs 9%; p = .21) AKs. CONCLUSION: Imiquimod postcryotherapy may increase clearance of subclinical and total AKs and proportions of subjects clear at 3 months. These findings require confirmation in larger controlled trials powered for statistical significance.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.363
Teacher spread0.318 · 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.

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".

Quick stats

Citations32
Published2007
Admission routes1
Has abstractyes

Explore more

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