Variability Intolerance

One of the fundamental purposes of diagnostic radiology is to differentiate between “normal” and “abnormal.”

Sometimes it’s easy, not even requiring a rad’s skills. For instance, a severely comminuted, mal-aligned fracture on XR: An erstwhile mentor of mine would have called it a “janitorial diagnosis,” meaning that a janitor, sweeping up at the far end of the reading room and glancing up to see the pics, would say, “Yep, that’s broken,” and resume sweeping.

An awful lot of the time, things aren’t so clear-cut. One learns in med school that there’s a range of “normal,” and comes to use phrases like “within normal limits” to reference this. Many even memorize certain ranges within their field of expertise. Most rads have a good idea of what constitutes a normal-sized heart on chest X-ray, when to call splenomegaly, renal atrophy, etc.

A preceptor of mine liked to point out the common practice of using bell curves to define normalcy. In the name of diagnosing and classifying things, “normal” is often defined as being within the range of population-mean plus or minus two standard deviations. Majority rules: the central roughly 95 percent of the curve gets called normal, and the top and bottom 2.5-percent tails are “abby-normal.”

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It makes a certain amount of sense. If you’re looking at a CT and the spleen is bigger than it is in 97.5 percent of the rest of humanity, there’s a decent chance that something’s amiss. Might it still be normal for the patient? Of course. It could be constitutional. If you investigate causes for asymptomatic splenomegaly and come up with nothing, at some point it becomes reasonable to leave it alone. Rather than insist that it’s too big and needs to be hacked out for the pathologist to scrutinize.

Which sometimes flies in the face of what we rads do. If established parameters say that organ X should be no more than 10 cm, and you call 12 cm normal on a scan, be prepared for someone to successfully zing you on quality assessment (QA). Even if you have scans going back 20 years showing it has always been this size, you’re still only safe from QA if you state that, yes, it’s larger than usual, but stable.

The preceptor mentioned above noted the sometimes absurdity of bell-curve thinking. Is it abnormal if someone is more than two standard deviations above the mean for height? IQ? No? Well, then, how about if they’re below the mean by more than two standard deviations? There’s a certain value-judgment going on when we decide that being in extreme percentiles at one end of the curve is “good” and the other is “bad.” Even if we substitute clinical-sounding terms like “adaptive” and “maladaptive.”

There’s also the matter of how much the bell curve used for diagnosing someone should be tailored to their particular circumstances. Assessment of height, for instance, is a lot less meaningful if you use a single curve for the entire population as opposed to separate curves taking things like gender and ethnic background into account. Except every time you want a curve correcting for some additional factor, you have to set about gathering and analyzing data for it…and your target-population gets smaller and smaller, diminishing the statistical power of it all.

And yet, each such refinement gets a step closer to something most of us learned in childhood: Everyone is different, and it’s not necessarily accurate or fair to judge them via standards based on others who don’t share those differences.

One might imagine a diagnostic rad, and indeed other professionals (medical or not), would maintain a greater awareness of this than most. We know what happens when wrongly proclaiming or even implying abnormality: Unnecessary further workup, needless treatments, patient worry, and of course dreaded dings to our QA stats.

That awareness goes away entirely too easily when we’re not practicing our profession. Maybe social media deserves some of the blame, but it seems to me that we’re awfully intolerant of variation from our personal norms when we’re not seeing them via PACS. There’s a pervasive “I’m normal/reasonable/right and anybody differing from me isn’t” attitude afoot in society, and rads are not immune:

Someone’s radiology gig differs from your own (tele versus onsite, academic versus private practice, 1099 versus W-2)? They’re wrong. Stupid, lazy, greedy, mercenary, gullible, naïve, selfish, pick your pejorative.

You overhear an exchange of advice between a young rad and a more-seasoned one, or see it in an online forum. The older rad’s experiences, or his interpretation of them, don’t mesh with your own? Or the younger rad’s interests don’t match your values? So easy (and common!) to condemn/dismiss them based on your own subjective reality.

A colleague bought himself a McMansion, a nice boat, or a sports car, while you prefer to live well beneath your means in the name of saving up and future-proofing yourself? What an idiot that guy is, right? Or maybe you’re the one living lavishly, and you see your more-frugal workmate as a pitiable, joyless Scrooge.

Someone strolled out onto the conversational minefield of politics and doesn’t see eye-to-eye with you on the issues? Voted for a candidate/party you didn’t like? How readily people allow a throwaway comment or two to derail relationships that were built up and enjoyed for years and years. It can’t possibly be that they formed their opinions just as reasonably as you did yours, based on different perceptions, values, and previous experiences, can it? Nah, there’s got to be something wrong with them.

My suggestion? Try to remember that “within normal limits” applies to a lot more than you can see on PACS. What might be abnormal or wrong for you might be perfectly reasonable for someone else. Hating and/or lashing out at them, even removing them from your life, might be excessive “otherization” on your part. Or, when they demonstrate their own variability intolerance towards you, a little charitable understanding might help you to not respond in kind. Indeed, your demonstration of greater variability tolerance might just help them rediscover some of their own.

Follow Editorial Board member Eric Postal, M.D., on Twitter, @EricPostal_MD.