A Phase 4, Open-label, Single-arm Study Evaluating the Effectiveness and Safety of Suzetrigine As Part of Multimodal Therapy for Acute Pain After Laparoscopic Procedures of the Intraperitoneal or Retroperitoneal Cavities or Arthroscopic Orthopedic Procedures
Abstract
The purpose of this study is to evaluate the effectiveness, safety, and tolerability of SUZ for acute pain after laparoscopic procedures of the intraperitoneal or retroperitoneal cavities or arthroscopic orthopedic procedures.
Conditions: Acute Pain.
Primary outcome: Proportion of Participants Reporting Good, Very Good, or Excellent on a Patient Global Assessment (PGA) for Pain Control at the End of Treatment.
Enrollment: 47 participants.
How strong is the evidence?
Evidence Hub reads this as a clinical trial, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 47 participants or records.
Read the paper
Related studies
- Noninvasive and Point-of-Care Surface-Enhanced Raman Scattering (SERS)-Based Breathalyzer for Mass Screening of Coronavirus Disease 2019 (COVID-19) under 5 min
- De Novo Transcriptome Analysis and Detection of Antimicrobial Peptides of the American Cockroach Periplaneta americana (Linnaeus)
- Molecular Characterization and Expression Profiling of Tomato GRF Transcription Factor Family Genes in Response to Abiotic Stresses and Phytohormones
- Assembly of Tight Junction Strands: Claudin-10b and Claudin-3 Form Homo Tetrameric Building Blocks that Polymerise in a Channel-Independent Manner
- Update of the Pompe variant database for the prediction of clinical phenotypes: Novel disease‐associated variants, common sequence variants, and results from newborn screening
- Machine Learning-Based SERS Chemical Space for Two-Way Prediction of Structures and Spectra of Untrained Molecules
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.