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A Pooled Analysis of the Safety and Efficacy Results of the Multicenter, Double-Blind, Randomized, Placebo-Controlled Phase 3 REFINE-1 and REFINE-2 Trials of ATX-101, a Submental Contouring Injectable Drug for the Reduction of Submental Fat

2014· article· en· W2330047397 on OpenAlexaboutno aff
Steven Dayan, Derek Jones, Jean Carruthers, Shannon Humphrey, Fredric S Brandt, Patricia Walker, Daniel Lee, Paul F. Lizzul, Todd M. Gross, Frederick C. Beddingfield

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicBody Contouring and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePlaceboAdverse effectClinical endpointRandomized controlled trialInternal medicineMagnetic resonance imagingContouringClinical trialPhases of clinical researchMulticenter studyNuclear medicineUrologySurgeryGastroenterologyRadiologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: ATX-101, a proprietary, synthetic version of naturally-occurring deoxycholic acid, is an investigational injectable drug under development for the reduction of excess submental fat (SMF). ATX-101 causes focal adipocytolysis when injected into subcutaneous fat. STUDY DESIGN AND PURPOSE: Two independent, identical, multicenter (US/Canada), randomized, double-blind, placebo-controlled phase 3 studies (REFINE-1 and REFINE-2) were conducted to evaluate the safety and efficacy of ATX-101. The pooled analysis from these two studies is presented here. METHODS: Patients (N=1022; 1019 treated with ATX-101 or placebo) with moderate-to-severe SMF, as rated by clinicians and patients, were randomized (1:1) to receive subcutaneous injections of ATX-101 (2 mg/cm2) or placebo into the SMF for up to 6 treatment sessions, approximately 28 days apart. Primary endpoints were changed from pre-treatment to 12 weeks post-treatment in (1) percentage of patients with a ≥1-grade change in the Clinician-Reported (CR) and Patient-Reported (PR) Submental Fat Rating Scales (SMFRS) composite and (2) percentage of patients with a ≥2-grade change in the CR-SMFRS/PR-SMFRS composite. Secondary endpoints were (1) mean change from baseline in the PR Submental Fat Impact Scale (PR-SMFIS) and (2) reduction in SMF volume by magnetic resonance imaging (MRI) in a subset of 449 patients. Adverse events (AEs) were monitored throughout. RESULTS: ATX-101 resulted in statistically significant reductions in SMF. The percentage of patients with a ≥1-grade change in the CR-SMFRS/PR-SMFRS composite was 68.2% for ATX-101 vs. 20.5% for placebo (p<.001). The percentage of patients with a ≥2-grade change in the CR-SMFRS/PR-SMFRS composite was 16.0% for ATX-101 vs. 1.5% for placebo (p<.001). ATX-101 treatment decreased the PR-SMFIS total score from baseline (7.3) compared with placebo (7.3; 3.7 vs. 1.3; p<.001) and increased the percentage of patients attaining a pre-specified reduction in SM volume by MRI (ATX-101 vs. placebo: 43.3% vs. 5.3%; p<.001). Most AEs were transient, mild or moderate in severity, and associated with the treatment area. The most common AEs were pain, swelling/edema, hematoma (bruising), anesthesia (numbness), all of which were expected as a result of the pharmacologic action of the drug. Only 1.4% of patients discontinued the studies due to AEs. CONCLUSION: The pooled analysis of the REFINE-1 and REFINE-2 phase 3 trials supports the efficacy and acceptable safety profile of ATX-101, a potential first-in-class injectable drug for contouring the submental region (Table 1).Table 1: Pooled analysis from REFINE-1 and REFINE-2 pivotal phase 3 trials of ATX-101 vs. placebo.CR-SMFRS, Clinician-Reported Submental Fat Rating Scale; PR-SMFS, Patient Reported Submental Fat Rating Scales; PR-SMFIS, Patient-Reported Submental Fat Impact Scale; SMF, submental fat; MRI, magnetic resonance imaging

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)0.021
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.025
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0100.015
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.029
GPT teacher head0.289
Teacher spread0.260 · 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 designMeta-analysis
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

Citations13
Published2014
Admission routes1
Has abstractyes

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