E-norms and AI in clinical neurophysiology.

Objective: To describe the use of Artificial Intelligence (AI) to automate the e-norms method, a technique used to derive normative data from patient studies, mixed datasets that contain both normal and abnormal data. Multiple studies have shown that normal values collected with the e-norms method compare favorably with those collected from healthy volunteers using traditional methods.

Methods: OpenAI's ChatGPT was used by the author to build a Python script to automate the e-norms method's plateau identification, the area of the e-norms curve where a variable's normal values lie.

Harnessing historical data to derive reference limits - A comparison of e-norms to traditionally derived reference limits.

Objective: Nerve conduction studies (NCS) require valid reference limits for meaningful interpretation. We aimed to further develop the extrapolated norms (e-norms) method for obtaining NCS reference limits from historical laboratory datasets for children and adults, and to validate it against traditionally derived reference limits.

Methods: We compared reference limits obtained by applying a further developed e-norms with reference limits from healthy controls for the age strata's 9-18, 20-44 and 45-60 years old.

Body mass index changes: an assessment of the effects of age and gender using the e-norms method.

Background: To validate e-norms methodology in establishing a reference range for body mass index measures. A new method, the extrapolated norms (e-norms) method of determining normal ranges for biological variables is easy to use and recently was validated for several biological measurements. We aimed to determine whether this new method provides BMI results in agreement with established traditionally collected BMI values.