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Challenges in Predicting Singular Events and the Use of Causal Benefit Model
Predicting singular events, such as the likelihood of having an infarct, presents challenges because it's either going to happen or not, rather than having a measurable probability. Different algorithms may provide varying risk percentages, but the actual outcome could still go either way. Additionally, advising treatment based on a risk threshold may not be a strong incentive for individuals to understand their actual likelihood of experiencing the event. To address this, a causal benefit model has been developed to measure non-HAL or APOB and project the risk over a longer period, such as 20 or 30 years. By providing individuals with a more relevant risk percentage, such as a 30% chance of a stroke before the age of 65, it gives them a meaningful and actionable number to consider.