COVID vs. COVID (part 1)

A quick comparison of probabilistic COVID scores as derived from application of machine learning (MLP) with COVID diagnoses as recorded in the EPR

Part 12 of this series packed quite a punch, so I’ll just do a quick plain English recap to ensure we’re all up to speed. What I’m doing is using a popular statistical technique within the medical sciences (Logistic Regression) to determine factors that influence the incidence of acute respiratory conditions (pneumonia, respiratory failure, respiratory arrest, ARDS) in patients who went on to die. The technique simultaneously takes account of age, sex, prevalence of COVID-19, major comorbidities, complexity of case, PCR test result, vaccination status, and the many interactions between all these.

When we replace the declared diagnosis of COVID-19 within the electronic patient record with a probabilistic function derived from a sophisticated model designed to predict presence of a genuine infection from a diagnostic array (true positive) all notion of vaccine benefit evaporates and we are left with results that provide evidence of vaccine harm.

In essence, I am using statistical methods …