Adverse Events Module

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Adverse Events Module

For researchers submitting trial data to ClinicalTrials.gov, the Adverse Events module is one of four mandatory results sections. It requires reporting in three primary categories: All-Cause Mortality: A table tracking all deaths that occurred during the study, regardless of cause. Serious Adverse Events (SAEs): A tabular summary of events resulting in death, life-threatening conditions, hospitalization, or significant disability. Other Adverse Events: A table for non-serious events that exceed a specific frequency threshold, such as 5% within any study arm.

Adverse Events Module path is as follows:

Study -> Results Section -> Adverse Events Module -> Event Groups

Study -> Results Section -> Adverse Events Module -> Serious Events

Study -> Results Section -> Adverse Events Module -> Other Events

Adverse Events Module

Ignite Creation Date: 2025-12-25 @ 2:36 AM
Ignite Modification Date: 2025-12-26 @ 1:13 AM
NCT ID: NCT04043234
Description: None
Frequency Threshold: 0
Time Frame: 35 days
Study: NCT04043234
Study Brief: RESCU System for Robust Upper Limb Prosthesis Control
Event Groups(If Any):

Event Groups

Title Description Deaths # Affected Deaths # At Risk Serious # Affected Serious # At Risk Other # Affected Other # At Risk View
Experimental The Experimental device includes the RESCU controller, Apple iPad, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow). RESCU: Retrospectively Supervised Classification Updating (RESCU) is founded on two innovations that promise significant improvement in performance and outcome. The first is a highly robust machine intelligence algorithm, an Extreme Learning Machine with Adaptive Sparse Representation (EASRC), and the second is a novel adaptive learning algorithm and communication interface we call Nessa. We contend that these two technologies allow the prosthetic device to adapt to its user from the initial fitting through continuing, long-term use in the activities of daily living, shifting the paradigm of training from the current prospective data gathering methods to a more dynamic retrospective application. 0 None 0 4 0 4 View
Control The Control device includes the pattern recognition controller, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow). Pattern Recognition: Pattern recognition prostheses associate the patterns of activity of multiple EMG sites to the action of a prosthesis. Such strategies have historically required prospective calibration of the EMG activation patterns. 0 None 0 4 0 4 View
Serious Events(If Any):
Other Events(If Any):