Publications

Background and Aims Baseline Pain Variability (PV) is often cited as a key predictor of placebo response (1-3). This variability can be caused both by actual fluctuating pain levels and by an inability to accurately assess one’s pain. Some suggest that this latter source may indicate patients more prone to contextual influence and thus more

Objective: The expectations of improvement among subjects participating in a Randomized Controlled Trials (RCT) are one of the features most frequently associated with the placebo response. Consequently, it becomes a pivotal factor for understanding treatment effect, particularly in chronic pain RCTs such as Osteoarthritis (OA). There are multiple techniques to account for these expectations in studies,

Using aggregated site features computed with the MPsQ to allow a better monitoring of sites in clinical trials Objective:   The MPsQ is an assessment characterizing the profile of the subjects participating to a Randomized Controlled Trial (RCT) to predict their behaviors in the study (placebo response, adherence, …). This analysis aimed to determine whether individual responses collected through the MPsQ could

Cognivia team members contribute critical elements to a recent paper published in Innovations in Clinical Neuroscience. Cognivia co-authored a recent paper published in Innovations in Clinical Neuroscience. The paper, “Using Artificial Intelligence-based Methods to Address the Placebo Response in Clinical Trials,” was a collaborative effort by leaders in artificial intelligence and machine learning, as well as their applications within

Purpose  Outcomes in Randomized Controlled Trials (RCT) are often computed as changes from baseline of finite scales (e.g., pain assessments going from 0 to 10). When subjects receive effective treatment over time they may reach the limit of a finite scale. In that case, the score limitation at the bottom of the finite scale, called