Admittedly logic can be a dry and boring subject and so can health when health is good. When health starts to fail however, panic ensues and for some reason, when we need it most, this is the time that we tend to throw logic out the window.

This article sets out to address the two ways that we throw logic out the window when we get sick. These are the two logic problems that stop us from understanding health and immunology. The first is how we understand (or rather, misunderstand) the name of the medical condition. The second is how we use (or rather, misuse) clinical data to reinforce understanding.

Logic Problem 1: The Name Versus the Cause

After considerable suffering and a reasonable amount of diagnostic effort, sometimes we are given a diagnosis: the name of our troubles. Diabetes, multiple sclerosis, CIRS, asthma, IBS, food allergies, celiac, eczema and a hundred others. There is displeasure in having some name applied to us, we who think ourselves invulnerable, but there is relief at least that finally we know what is causing our symptoms.

This relief is completely illogical. Having been given the name of our symptoms we are absolutely no closer to understanding the root cause than we were before because the name is only a summary.

Let’s play a short game to illustrate this: the diagnosis game. In the following list of symptoms, try to come up with the name of the condition the person has.

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The Diagnosis Game

Questions

1. Can’t digest gluten? (your answer goes here…)

2. Can’t breathe very well?

3. Can’t metabolize sugars or carbohydrates?

4. Progressive loss of motor control with associated brain lesions?

5. Get inflamed and in pain from food, air and water and nobody can figure out why?

The Diagnosis Game: Answers

1. Celiac; 2. Asthma; 3. Diabetes; 4. Multiple sclerosis; 5. CIRS (chronic inflammatory response syndrome).
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If you guessed all five answers correctly, you might want to pause before congratulating yourself. Do the names really cause the conditions they are describing?

Think about this a different way. When it is snowing outside, is that because of snowitis? Does the tree, devoid of leaves for the winter, have bare-leaf-ma? Is it cold because of cold-collect-around-a-mus? In fact we understand the weather very well so bare-leaf-ma is absurd, but when the term asthma is not equally absurd it shows how poorly we understand health.

We use logic with the weather but not with medical conditions. One of the most blatant examples is the medical condition COPD.

COPD, a short case study

A fellow finds it progressively harder to breathe. He goes to the doctor and says:

“Doc, for the longest time, I have been finding it hard to breathe”.

After a series of tests (basically he blows into a tube) and a referral to a pulmonologist, the diagnosis is in: he’s got COPD.

“What’s COPD?” the fellow asks.

“Well,” says his doctor, “For the longest time means this has been a chronic issue. And the fact is, something is in there, and that’s an obstruction. The Latin word for lungs is pulmo and from that we get pulmonary. And overall the symptoms are not good so you’ve been upgraded from an –itis or a –syndrome to a full disease. Now “Chronic Obstructive Pulmonary Disease” is a mouthful at the best of times so we call it COPD.

“But Doc,” the fellow says. “I came in telling you that for the longest time I haven’t been able to breathe well, and now you’re telling me that for the longest time I haven’t been able to breathe well. You’re just adding in the Latin word for lungs and calling it a disease.”

“Certainly,” says the Doc. “That’s what COPD is. And considering how long we’ve been at this today, there’s a good chance that your car out front has parking ticket-itis. You might want to go and check…”

THE LOGIC

This might sound like a joke, but why can’t our friend Bob digest sugars? He’s got diabetes. Why can’t Amy walk well anymore? Multiple sclerosis. Why can’t the fellow in the example above breathe? COPD and if not, asthma. He’s got something, right?

But while the name is a very reasonable description of the symptoms, it cannot also be the cause of the thing it is describing, anymore than the phrase ‘snow on the ground’ can both describe the collection of crystallized water and be the cause of that process. This is not the way of logic.

In the panicked search for the name of our symptoms, we stop looking once we have found the name and unfortunately, we stop short of finding the actual cause. And if we push further against this naming process, we only come up against another name: autoimmune. Autoimmune does not cause autoimmune issues, anymore than COPD causes COPD, asthma causes asthma, or diabetes causes diabetes.

THE SOLUTION

Many people that I speak to in consultations, or that reach out to me over email or through my website express that they are frustrated that they have been given a name to summarize their symptoms. They seem to be preoccupied with fighting against the name itself, as if the name is some sort of lie that needs to be disproven. I want for them and you to understand that there is nothing technically wrong with a name. A name is a word used to describe your symptoms so that others can support you better and the fact is that your doctor is legally required to deliver an accurate diagnosis: an accurate name. Not doing so would constitute medical malpractice.

The thing to understand is that a name is a description, but it is not a cause.

The solution to this logic problem is that a name cannot be a description of symptoms and also the cause of the thing it is describing. A name cannot play two roles. It is only a description, it absolutely does not also cause what it is describing.

Logic Problem 2: The Data Versus the Cause

Whether health data has driven us to a name, or whether the name has driven us to gather data, ultimately our understanding of a condition comes down to the data.

We can look at blood glucose levels, immune system markers like IgA or IgG, pulmonary functioning, do a detailed optical analysis, look at the skin under a microscope, scope the stomach and duodenum, get a colonoscopy, do a biopsy or run a complete blood panel: check for ferritin, vitamin D, red blood cell count, triglyceride levels, the ratio of bad to good cholesterol. We can do a heavy metal test, get a live blood cell analysis or get our food allergies checked.

Finally, we imagine, we have got to the cause of our symptoms. Diabetes is caused by high blood sugar; Eczema is caused by a scaly rash on the skin; COPD by reduced pulmonary functioning; MS by altered nerve functioning and brain lesions.

But is this logical? We legitimately analyze various health metrics to understand our symptoms but can the metric both quantify the symptom and also be the cause of the thing it is quantifying?

This is only a more detailed extension of Logic Problem 1 above. No, a metric cannot be the quantification of the symptom and also the cause of the symptom. To use the above example of the snow, it is like saying that there are exactly 21 inches of snow and the temperature is precisely 20 degrees Fahrenheit, so of course that is why the back yard has snow. (And those strange yellow patches in the snow? No idea but they’re 6 inches around, smell funny and have paw prints leading away from them… Now the snow has yellow patch-itis. Yikes!)

The data is not the cause of the symptom it is quantifying, anymore than the name can be the cause.

Illogic

I think that more and more people are becoming aware of this fundamental lack of logic in our approach to understanding health and symptomology, and this is one of the drivers that leads many to explore alternative healthcare options. In some of those cases, a person might want to explore muscle testing.

Very simply summarized, muscle testing is the process of pushing on someone’s arm to evaluate their strength, then introducing a chemical stimulus to see if it alters the person’s bioelectric field in some way, and then pushing on their arm again to see if there has been an alteration in strength. If there has, it is possible to use logic to draw certain specific conclusions about the relationship between the person and the chemical stimulus.

Muscle testing only works if it is used according to rigorous logical principles. It would be better if a practitioner were first trained in formal, truth functional logic and only learned about muscle testing later on, than if they were trained in muscle testing first and only learned about logic later on…

MISUSES OF MUSCLE TESTING

There are too many misuses of clinical muscle testing to list. The top few that come to mind are muscle testing for energetic blockages, past life karma or the practitioner asking a question in their mind and pushing on the patient’s arm to see what happens. Or pushing on a completely different person’s arm. Or pulling their own fingers apart. Or doing any of this over the phone. These are not scientific, reproducible or logical and have no place in a clinical setting.

Premises in Logic

Logic by contrast is clean, crisp, clear and reproducible. Where logic goes astray however is in the matter of what premises we adopt.

A premise is an assumption that is the basis for a conclusion. If all cats are grey and this animal is a cat then it must be grey. What happens to our theory then when we see a brown cat? Either the premise that all cats are grey is wrong, or this must not be a cat. But it is a cat… so our premise must be wrong.

In this way we can use logic to compare our assumptions with reality to ensure we are basing our conclusions on accurate premises.

Muscle Testing as Data Gathering

Muscle testing in itself is not remarkable. As stated, it is merely the act of pushing on someone’s arm. The data that we can gather from muscle testing is remarkable because some of it is not available from any other source. Thus the conclusions we can draw from a muscle testing analysis are sometimes extraordinary, not because of any special quality in muscle testing itself, but because of the extraordinary nature of the data.

Here is an examples, using the above case study of COPD.

1. We start with a positive baseline muscle test. This means the person being tested has passed the test, and is alive.

2. We place a hand on their chest over the lungs, or they place their own hand on their chest over the lungs. The hand is necessary because the human hand produces its own strong bioelectric field and acts as an electrical stimulus, kind of like ringing a bell or ground penetrating radar. It would not work if we placed a book on their chest. A book might weigh as much as a hand, but does not produce an electric field. If you want to feel this field, by the way, place your hand an inch away from your own cheek and feel the warmth. That is partly body heat you are feeling, but partly also bioelectricity.

3. Building on the concept of the hand acting as a type of ground penetrating radar for the body, if the lungs of the person being tested are in a state of inflammation, placing a hand over them will act as a trigger and reduce their overall bioelectric field functionality (or it would amplify the dysfunction, figuratively these are two different sides of the same coin). The result is that the original strong muscle test from step one above will reverse into a weak muscle test. This is the body’s way of responding to a stressor. Think of a turtle. If you tap on its shell it pulls its head inside the shell, reducing its vulnerability. The body is neurologically wired to perform and respond in the same way. In my book Experiments in Muscle Testing, I call this the turtle response (p. 54)

It might seem that after all this effort, the muscle test has only showed us what we already know: that the lungs of a person with COPD are inflamed, or that for example gluten is a stressor for someone with celiac, or that the motor cortex is inflamed in someone with MS. On the surface, it sounds like we are drawing a circular conclusion and this might seem no better than the two logic problems above, where [Problem 1] the name is mistakenly thought to be the descriptor and [Problem 2] the health metric the cause of what it measures.

In fact the weak response we can elicit from a muscle test is not where muscle testing ends but where it begins. From there we can build on the negative test by constructing a data set based off of it.

Gathering Data

Once we have established the baseline measurement that a person’s arm will produce a weak muscle test relative to a reproducible stimulus (such as placing a hand over the lungs, or over the motor cortex, or relative to a food trigger like gluten), we can build on this by introducing a series of chemical stimuli to see which ones interact with the person’s unique biochemistry.

Here are some examples of the sorts of data that we can gather using the above case of COPD. It may seem random but as we will see, there is a way of making sense of it.

COPD Data: Lungs muscle test weak. Chemicals that cancel out the weak test are:

1. The amino acids cysteine and collagen
2. Vitamin E
3. Vitamin B1
4. Beta carotene
5. Zinc citrate, a nutritional mineral
6. Elemental oxygen (O#8), argon (Ar#18) and indium (In#49)
7. The antibiotic rifampin
8. The following antiparasitic medications: nitazoxanide, mebendazole, praziquantel
9. The following antifungal medications: fluconazole, itraconazole

How to make sense of it all? “Down, down, down, would the rabbit hole never end?”.

Assembling Data Sets

Once we have gathered the chemical data that is specific to this individual we can further use muscle testing to organize it into data sets. I would recommend that we categorize this based on the chemistry for reasons that will become clear shortly. Relative to this individual:

1. Oxygen (O#8) muscle tests against the antibiotic rifampin, the antiparasitic medication nitazoxanide, vitamin B1 and betacarotene, a fat soluble vitamin that the body uses as an antioxidant.

2. Argon (Ar#18) muscle tests against mebendazole and zinc citrate.

3. Indium (In#49) muscle tests against vitamin E, praziquantel and fluconazole.

Based on these categories we can insert a few premises and then draw some reasonable conclusions.

PREMISES:

• The antibiotic rifampin treats bacteria such as (but not only) tuberculosis so if someone is testing for rifampin, they must be a host to some bacteria that rifampin treats. (This is a reasonable assumption, right?)
• Since nitazoxanide, mebendazole and praziquantel treat parasites (protozoans, roundworms and flukes respectively), if someone is muscle testing for nitazoxanide, mebendazole or praziquantel, then they must host some parasites that nitazoxanide, mebendazole and praziquantel treat.

DATA SETS:

Data Set 1, Oxygen (O#8): We have probably found a type of protozoan and also a type of bacteria that uses oxygen, vitamin B1 and beta carotene. These may be related to or separate from one another, this is not clear from the existing data but what is clear is that we all need oxygen to breathe, and this person with COPD can’t breathe, so maybe if we’re finding parasites and bacteria, related to each other or unrelated, that are known to have a metabolism based on oxygen, this may partly at least account for why the fellow is having breathing difficulties. This is the sort of thing that would constitute an actual cause of not being able to breathe, and for anyone interested in the chemical pathway, B1 (thiamine) plays a major biological role in oxygen metabolism and would be an example of the biological mechanism by which a parasite or bacteria would undermine breathing.

Data Set 2, Argon (Ar#18): We have probably found a type of roundworm that is using argon and zinc in ways that are not understood. Argon is not understood to play any biological role but it does exist at substantial levels in breathable air (0.93%) and thus we inhale a considerable amount of it. As a fat soluble gas it may be playing some role in pulmonary functioning that has to do with facilitating gas transport across a lipid membrane in the alveoli, and there is clearly a worm associated with it and with zinc citrate, the mineral backbone of most of the enzymes in our body. There is a cross relationship with fluconazole, an antifungal medication. Enzymes – gas synthesis – fungal infection – breathable argon… could be worth looking into.

Data Set 3 Indium (In#49): We have probably found a type of fluke that has a metabolism based on indium. Indium is not understood to play any biological role, so this looks like an unusual finding on the surface, but it may play a crucial part in oxygen synthesis. The fact is that when industrial workers get exposed to indium in color TV factories (it is used industrially to make blue pigments) they develop a condition called Indium Lung. They don’t get indium foot, they get indium lung and thus we can infer that elemental indium is somehow involved with the lungs in a way that is not well understood, but worth looking into. There is certainly a trace level of indium in each of us–about 0.4 mg, which is a huge amount in terms of biochemistry. We get trace levels of it in our diets from beef, dairy, wheat and peppers. It is not a stretch of the imagination that some fluke has found a way to metabolize indium in a way we don’t understand, and certainly, a lung fluke, indium or otherwise, would hinder rather than help someone’s breathing. One might even go so far as to say that a lung fluke could hinder breathing chronically, in an obstructive sort of way, almost causing a pulmonary disease…  

So we have three possible data sets pertaining to this person’s lungs and they all trace back to a parasite, a bacteria or both that is altering the biochemistry that is essential for functionality in this area.

Logical Synthesis

We have three data sets that each point to a possible cause of this hypothetical person’s symptoms. Even then we cannot assume cause and effect. We must summarize the information in the following logical manner:

It may or may not be the case that the parasites and bacteria in data sets one, two and three are the cause of this person’s COPD.

From a logical standpoint, each of these is a reasonable possibility, and when all three are found together in the same symptomatic location it seems almost probable that there is a correlation between the data sets and the poor fellow’s symptoms. There is no certainty that there is a cause and effect relationship, and simply from a logical standpoint, there will be no certainty. Logically all we can be sure of is that we may or may not have found the root cause, and that it is worth looking into further.

But at least now we are having a conversation about an actual possible root cause and a course of action that might conceivably change the outcome. At least we are having a technical conversation based on sciency things like chemistry, parasitology and bacteriology. Sure, this is not conclusive and sure, it would be ideal if some parasite would crawl out of the guy, slither under a microscope slide, wink at us and leave its business card. And yet, in this less than ideal world we all live in, isn’t it better to at least try to be logical than proceed based on the clear impossibilities that the name is causing the symptoms, or that some blood markers are the causes of the things they demarcate?

At least now we are trying to be logical.

Lewis Carroll

You may recognize the illustrations in this article from Lewis Carroll’s iconic children’s book Alice in Wonderland (1865), the famously illogical fantasy story about card soldiers, March hares, mad hatters and a girl who fell down a rabbit hole. In addition to authoring fantasy stories Carroll (1832–1898) was a mathematician who specialized in mathematical logic (today called formal logic or truth functional logic).

I think he would have been fascinated by the information in this article because in the 19th century they did not have access to muscle testing as a diagnostic modality. The logic is obvious once you have data, but a radical new means of gathering data and drawing clear correlations between sets of it is a game changer in any century. On the surface it seems simple: logic plus biomechanics. Cavemen could have used it… But because of its ability to establish causal relationships between chemical metrics and body locations, and draw inferences from parasitology, organic chemistry, bacteriology and physiology, muscle testing is the most sophisticated health technology in human history. I think as a logician Carroll would have instantly intuited that.

But I bet that he would have related to this article for a more personal reason as well. He dealt with breathing issues his whole life. He caught a bad case of whooping cough at age 17, and was known for having weak lungs as an adult. Indeed in 1898 at age 65 he contracted influenza and died of pneumonia shortly after.

To show the state of healthcare at the time, I should point out that we understand the word influenza to mean the flu virus, but in fact the flu virus was only discovered in 1933. Prior to that, something very different was meant by influenza. The word comes from the Latin “influentia” meaning “influence”. People used to believe that the movement of the stars could cause negative health symptoms. To say someone had influenza literally meant that they had the influence of the cosmos affecting them negatively. The formal medical diagnosis of the cause of death of one of the greatest writers in human history–Lewis Carroll–two years before the commencement of the 20th century was that he caught a case of the cosmos influencing him, and then developed pneumonia and quickly died. One imagines that it was only by chance that his cause of death was not ascribed to bad air, which in Italian was Mal-aria. This latter phrase gave us the word malaria that we know today to be a protozoan parasite, as I outline in this article.

The point in referencing Lewis Carrol’s health is to show that he was in the same boat 130 years ago that you might be today. He had a set of symptoms and instead of someone using a logical data set to break the problem down into manageable bits, and try to modify the bits to see what worked–instead, his symptoms were given a name with the clear illogical supposition that the name was, after all, causing the symptoms. And again, to really drive this point home, the name was influenza–that the cosmos had influenced him. If I had been him, a logic professor in 1898, I don’t know if I would have been more annoyed about dying from the influence of the cosmos or that a complete lack of logic had been used to arrive at the diagnosis.

Go down all the rabbit holes you want. Why not? They are fun, and who knows what Wonderland you might end up in. But take logic with you. Don’t pretend that the name or the data metrics are the root cause of symptoms. Instead, try to assemble your own logical data set using biochemistry, organic chemistry, molecular nutrition, parasitology and bacteriology. Muscle testing can help with this as long as it is used scientifically and based on truth functional logic.

For my part, now I must go. To quote the white rabbit, “I shall be too late”.