I think problems like the ones below would be alleviated by using logistic regression models, which do not have left-digit bias. "We hypothesized that doctors

I think problems like the ones below would be alleviated by using logistic regression models, which do not have left-digit bias. "We hypothesized that doctors may be overly sensitive to the left-most digit of a patient’s age when recommending treatment, and indeed, in cardiac surgery they appear to be. When comparing patients who had a heart attack in the weeks leading up to their 80th birthdays with those who’d recently had an 80th birthday, we found that physicians were significantly less likely to perform a coronary artery bypass surgery for the “older” patients. The doctors might have perceived them to be “in their 80s” rather than “in their 70s.” This behavior seems to have translated into meaningful differences for patients. The slightly younger patients, more likely to undergo surgery, were less likely to die within 30 days. Our study confirms previous work that found doctors are overly responsive to patient age when diagnosing illness, and that showed how seemingly irrelevant factors‚ such as the difference of a few weeks of age, could govern physicians’ decisions about treatment, with potentially life-altering consequences for patients. Left-digit bias could affect many clinical decisions. For example, patients with hemoglobin levels of 9.9 grams per deciliter may be perceived as being substantially more anemic than patients with hemoglobin levels of 10.0 grams per deciliter (the difference in the two values has no clinical significance)."