If you don't care about how to read these things and just want the bottom line, here's my non-professional, very-not-a-doctor conclusion in a TL:DR;

Her numbers are low, but not *really* low.

If you want to know how your doctor and I read these results, read on.

The results of any blood test are statistical in nature, that is, they are based on probabilities, not laser-specific values. Remember, we're taking a tiny little sample of blood from a pool several litres in size, and trying to make conclusions about that pool (and many other things) based on that tiny sample. And so it helps look at these results from a statistical perspective. Lucky for you, I'm a research engineer, and this is kind of my thing ;)

I know it sounds callous to categorize something so important as a daughter's health as "healthy", "sick", "really sick", "kinda sick", etc, but in the end, if you were to take every test, every scan, every measurement possible and look at the list of results, you can't say someone is 51.235% healthy, there's no way to combine all of the results in a meaningful way. And so, we're left with this subjective decision of "how low is too low?"

When you see those reference values beside the measured value, it's important to note that those aren't cut-and-dried thresholds, for example, saying that a person with a WBC (white blood-cell count) of 4.0 is healthy and one with 3.999 is sick. These are a probabilistic spectrum, in most cases a "normally" distributed range. If that sounds familiar, it's because the vast majority of statistical methods are based on the "normality" of data. Here's why: tinyurl.com/qrwq58z it's called the "normal curve", aka the "bell curve" or to us nerds, the "Gaussian" curve. When you can assume that a population is normally-distributed, then you can make more confident conclusions about samples taken from a small subset of that population. When we talk about how "normal" datasets are, you'll hear three very important terms: "Mean" (also called "Average"), "Standard Deviation" (also "Variance"), and "Confidence".

Remember, when we're looking at these blood tests, we're trying to decide how sick the patient is, relative to the average person. And so the dataset that these reference ranges come from include a huge array of people, so huge that age, gender, race, and all the other factors that make us individual are washed out. Furthermore, these values can go up as well as down, so our reference range has to go both ways (except for some tests, where the normal is zero or close to zero, in which case the scale only goes up and all the statistics change...  this is not the case here). And so the midpoint of our reference range -- the halfway mark between "low" and "high" -- is our population mean.

The Standard Deviation describes the spread of the values measured from our reference population. It's tricky to put into words, let's see if I can do it better with an example: If you were to measure the height of every person in Canada, the average adult male would be about 5'9", and the standard deviation is approximately 3". Looking at the normal curve again (tinyurl.com/qrwq58z), you can see that 68.3% of Canadians would be within one standard deviation of the mean (from 5'6" to 6'0"), and 95% of Canadians would be within two standard deviations (from 5'3" to 6'3"). If you need more explaining here, let me know. Just so we don't go crazy typing the words "standard deviation" over and over again, statisticians use the greek letter sigma is used to represent this term, or, in cases like this where we can't actually type the symbol, we just use the word "sigma".

The flip side of standard deviation is called the Confidence Interval. It describes how likely our decisions are wrong, or how  confident that we're right. This is really valuable when we are trying to decide if a sample is from a larger population or not. If (and I know this sounds silly) you were to try to determine if someone is Canadian based on their height, if you know the mean (5'9") and standard deviation (3"), then you can say with 95% confidence that if they're taller than 5'3", then it's unlikely that they're Canadian. See how I said "unlikely" and not "definitively" there? With statistics, you have to acknowledge that everything has error.

OK, let's bring this back to your daughter's blood tests. In my experience, the vast majority of reference ranges used for "common" tests, like the CBC or a chem panel, have a 5% confidence interval. That means that 95% of the healthy population should fall within +/- 2 sigmas of each other, and the upper and lower limits of that range are the high and low reference values you see on that page.

Let's look at the WBC count first. Your daughter's results indicate a measured WBC of 3.8, and the reference range is from 4 to 11. The midpoint of that range is 7.5, and (assuming a 95% confidence interval and a normally-distributed range), then the entire range spans 4 sigma, meaning one sigma is 1.75. We can used these values to determine how "far" your daughter's WBC value is from the mean, and compare it to the distribution of our healthy reference population. If you subtract your daughter's score from the mean (7.5 - 3.8 = 3.7) and divide it by one sigma (3.7 / 1.75 = 2.115), then we see that her WBC count is only 0.115 sigma from the healthy range, and that's not very much.

Using the same method to look at her neutrophils (only 0.218 sigma out of range), once again it's not *that* far out from out 95% confidence interval, but slightly moreso than the WBC. The RDW score is also not *that* far out from the healthy range (just 0.533 sigmas), but it's starting to border on concerning.

What's more important at this point is to consider the nature of these values. Neutrophils are the first-responders of the white blood cells, and are generally the first type of WBC to accumulate wherever an infection is present. With that in mind, unless a blood sample is drawn directly from an infection, a low neutrophil count on a CBC might not indicate that there are too few of them, only that they're "busy". In medical terms, this is called "sequestering". The RDW score, or Red-blood-cell Distribution Width, describes the average age of the red blood cells in the sample. Red blood cells grow naturarlly as they age, and a lower-than-average RDW score means that the blood sample contained younger blood cells than what would be expected. This can mean that something is causing more red blood cells to be formed, or something could be killing off the older red blood cells earlier than normal. This sounds bad, but remember the typical lifespan of a red blood cell is 100 days for an adult, 80 days for an infant, and I don't know how old your daughter is, but she'll be somewhere in between.

To sum this all up, we have to remember that we're part of a much, much larger population of humans, and that there's this ambigious definition of "healthy", and we task our doctors to keep us as close to that definition as possible. However, the tools they have to work with are rarely black-and-white, and they only have ranges, references, statistics and scores upon which to make their decisions.

In my 100%-not-medically-trained opinion, I think your doctor's repsonse is the right one.

However, your doctor (hopefully) has more than one patient, and it's unfair to expect him to have perfect recall of your daughter's condition with every visit. If you want comfort in numbers, I would go back to every test result that you have, and start putting them into spreadsheets, and use formulas to turn the raw numbers into sigmas. Then you can visualize (aka graph) the scores over time. If you see a downward trend, make a note of it and bring your spreadsheets to the next doctor's appointment. If your doctor is the right kind of doctor (and assuming you did the math right), he should be able to take your concerns to heart and discuss how they affect your daughter's treatment, if at all. If he's dismissive of your efforts, or comments on the medical degree he has that you don't, or in any way doesn't seem to care that you see a trend he didn't ("you're reading too much" or "don't take the tests so seriously", both of which I've heard in the past...), then as much of a pain in the ass I know it will be, it's time to go looking for a second opinion... permanently.