The person who first put the concept of wizardry into perspective for me was Louis Perino, DVM, PhD, an original writer of this column and now an MD.

Perino described the wizardry concept as a fight between two extremes. “Paralyzing indecision” is where we refuse to make a decision without critically evaluated data to guide us. In the other extreme, wizardry results when we simply combine technology and personal experience to guide our actions.

In reality, we must function somewhere in the middle. However, some among us function solely in the wizardry mode.

Key concepts: Wizards have outstanding social skills and may or may not have advanced degrees. Second, for wizardry to flourish, it requires a wizard and a believer.

A good wizard:

  • is very popular with clients (and other wizards) who believe in wizardry,

  • can sell a lot of very profitable potions to their believers, and

  • isn't stressed by conflicting data. Here's how wizards fall from grace:

  • Another wizard may come along with a better show.

  • Potions are expensive, making it more difficult for the believers to compete, especially when the potions replace effective products or are harmful.

  • Believers may eventually figure out the wizard is a liability, not an asset.

To help you spot wizards, here are some items you'll find in their bag of tricks:

Essence of historical controls: The best time to introduce new potions is right after a major disease wreck. Wizards refer to this as “riding the epidemiological curve to glory.” They know the occurrence of disease tends to cycle in production systems. If they can slip the potion into the system after a very severe period in the cycle, they get credit for the upturn and create a lifetime believer. When a new believer encourages others to try the potion, it is referred to as “toe of anecdote.”

Historical controls are also in place when programs are changed at the start of a new year, such as adding or changing a product prior to the new fall run of calves. If things are better, we really want to believe our change of products was responsible.

Eye of titer: This is an easily defeated favorite of junior wizards with vaccines to sell. It is also known as a “substitution variable.”

While it's true that vaccine antibody titers indicate a quantifiable response to vaccination, they aren't necessarily indicative of protection or of a benefit in your production setting. Titers may be correlated to protection by using artificial challenge models (the disease is given to animals in a lab setting).

But, how does this model relate to your production setting? You must always ask the question: “Do you have clinical trial data in my type of production system to show a benefit?” And, more importantly, “What safety data do you have on this product?”

Immediately after asking this question, be prepared for sprinklings of essence of historical control and toe of anecdote.

It's amazes me that all questions regarding vaccine efficacy seem to disappear if the specific pathogen used in the vaccine is isolated from the producer's facility. This effect doubles if it's the latest fad pathogen. The same questions still apply relating to safety and efficacy.

Tincture of temperature reduction: For drug wizards, this is the equivalent of eye of titer. We all view fever reduction as a good sign in treatment of infectious disease in an animal. We hope it means the end of the temperature elevation caused by the pathogen (and, therefore, the pathogen), and the defensive actions of the immune system.

So if you're presented with data showing a drug decreases fever despite having no activity on the pathogen, you must again ask the question: “Do you have clinical trial data in my type of production system to show a real benefit?”

What to do

By now it's evident our protection against wizardry is centered on clinical trial data in a relevant production system. Unfortunately, there are both well-designed and well-conducted clinical trials, and poorly designed and conducted trials.

Some wizards counter the clinical trial argument by claiming the technology is too new for data to have been generated, or it's hard to show the difference within the “noise” of commercial production systems. In the next few columns we will help you to address these statements and to evaluate the trial data that's available.

Mike Apley is an associate professor in clinical sciences at Kansas State University.