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Detailing Bayes’ Theorem

If you come to us here at DeLuca & Weizenbaum LTD after having been misdiagnosed by a doctor in Providence County, you may question how a professional educated to spot the signs of distress or disease in patients could make such a mistake. A closer look at the diagnostic process may help pinpoint where errors may have occurred.

According to Medscape, many healthcare providers apply a probability test known as Bayes’ Theorem when determining the likelihood that you may have a disease. Your doctor first performs an evaluation to determine your pretest odds of having a certain disease or malady. Diagnostic sensitivity tests then screen for the potential presence of a condition, while specificity tests confirm its occurrence. The sensitivity and specificity rates of these tests are then used to formulate a likelihood ratio, or how likely a positive result indicates you do in fact have the condition in question.

Your posttest odds are then determined by multiplying your pretest odds by your likelihood radio. That number is then converted to a probability. The process is demonstrated below with a pretest probably of 25 percent, and a test with sensitivity of 85 percent and specificity of 90 percent:

  1.        Determine pretest odds: .25/(1 - .25)  =  .33
  2.        Determine likelihood ratio: .85/(1 - .90) = 8.5
  3.        Determine posttest odds: .33 x 8.5 = 7.64
  4.        Convert to probability = 7.64/(1 + 7.64) = 88.4 percent

As you can see, this can be a complex process. Your doctor may rely on aides to give him these numbers due to time constraints, yet these are not infallible. Overreliance on such aides as well as skipping important steps could lead to a misdiagnosis. You can find more information on this and other factors used to diagnose patients here on our site. 

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