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Final considerations

We took a long and tortuous path to better understand the complexity encountered by the colleague struggling with the very heavy ethical responsibility of making a diagnosis. However, this task becomes even more complex when we need to be detailed and careful in making a differential diagnosis.

Here, we enter a delicate topic, that is connected with the epistemological contents and that first of all was reported in the "Introduction". We are talking about:

• Interdisciplinarity:
  In science policy, it is generally recognized that science-based problem solving requires interdisciplinary research (IDR), as proposed by the EU project called Horizon 2020. In a recent study, the authors focus on the question why researchers have cognitive and epistemic difficulties in conducting IDR. It is believed that the loss of philosophical interest in the epistemology of interdisciplinary research is caused by a philosophical paradigm of science called "Physics Paradigm of Science", which prevents recognition of important IDR changes in both the philosophy of science and research.
  The proposed alternative philosophical paradigm, called 'Engineering Paradigm of Science', makes alternative philosophical assumptions about aspects such as the purpose of science, the character of knowledge, the epistemic and pragmatic criteria for the acceptance of knowledge and the role of technological tools. Consequently, scientific researchers need so-called metacognitive scaffolds to assist them in the analysis and reconstruction of how 'knowledge' is constructed in different disciplines.
  In interdisciplinary research, metacognitive scaffolds help interdisciplinary communication analyse and articulate how the discipline builds knowledge

This concept is linked to the previously discussed topic in which the colleague should be aware of his own 'Subjective Uncertainty' (due to a classic logic language 'sick or healthy') and of 'Objective Uncertainty' (due to a probabilistic logic language 'probably sick or probably healthy'). It is not complicated to prove this assertion: the uncertainty we are talking about derives from the fact that the elements, assertions, data, classes and subclasses mentioned and that build the apparatus of the logic of probabilistic's language: Analysandum $=\{P(D),a\}$ and Analysan $=\{P(D),a\}$ are elements that exist in a specific world, and in this case in a dental context in which the element $KB$ of the process indisputably indicates a "basic knowledge" only in a specific dental context.

This conclusion confirmed by the dentist was the following:

$P(D|Deg.TMJ\cap TMDs)=0.95$

or better: it is my 95% belief that Mary Poppins is affected by TMDs since she has a degeneration of the temporomandibular joint in addition to the positivity of the data $D=\{\delta _{1},\dots \delta _{n}\}$But something strange happens because out of nowhere, a researcher, who uses 'metacognitive scaffolds' for an implementation in the analysis and reconstruction of how 'knowledge' is built in different disciplines, demands an answer to the following question from the dentist:

 

«...is there another world or context, parallel to yours, in which in addition to the D data there are further data unknown to you?»

and increase the dose: submit Mary Poppins to the following trigeminal electrophysiological tests, label them as we did previously for the set data $D=\{\delta _{1},\dots \delta _{n}\}$ generating another set containing a number $m$ of unknown data (not belonging to the purely dental branch) $C=\{\gamma _{1},\dots \gamma _{m}\}$ thereby creating an entirely new set that we will call $S_{unknow}=D+C=\{\delta _{1},\dots ,\delta _{n},\gamma _{1},\dots ,\gamma _{m}\}$ (called $S_{unknown}$ precisely due to the presence of data unknown to the dental context).

$\delta _{1}=$ Positive radiological report of the TMJ in Figure 2

$\delta _{2}=$ Positive CT report of the TMJ in Figure 3

$\delta _{3}=$ Positive axiographic report of the condylar traces in Figure 4

$\delta _{4}=$ Asymmetric EMG interference pattern in Figure 5

${\gamma _{1}}=$ Jaw jerk in Figure 6

${\displaystyle \gamma _{2}}=$ Mechanical Silent Period in Figure 7

${\displaystyle \gamma _{3}}=$ CT right masseter muscle in Figure 8

Third Clinical Approach

(hover over the images)

• 

  Figure 1: Patient reporting "Orofacial pain in the right hemilateral)

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  Figure 2: Patient's TMJ Stratigraphy showing signs of condylar flattening and osteophyte

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  Figure 3: Computed Tomography of the TMJ

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  Figure 4: Axiography of the patient showing a flattening of the chewing pattern on the right condyle

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  Figure 5: EMG Interferential Pattern. Overlapping upper traces corresponding to the right masseter, lower to the left masseter.

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  Figure 6: Jaw jerk electrophysiologically detected on the right (upper traces) and left (lower traces) masseters

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  Figure 7: Mechanical silent period detected electrophysiologically on the right (upper overlapping traces) and left (lower overlapping traces) masseters

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  Figure 8: Axial CT of the facial massif in which there is a marked hypertrophy of the right masseter

In this way it has been shown that, inevitably,

«the logic of medical language is more or less based on data that derive from a specific world or context or rather, a specialistic context in which the perimeter that delimits this knowledge does not allow us to project ourselves into parallel contexts»

By exploring this perimeter line of the specialist context, we will create an area close to it which we will call the 'fuzzy zone' or 'fuzzy logic' which we will discuss in the next chapter.

 

«... from what it seems not even with a probabilistic language logic we will be able to define an exact diagnosis.» (in fact, for this reason we should also consider Fuzzy Logic Language)

 

The logic of classical language   Fuzzy logic language

Bibliography & references

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In this chapter, we will discuss the logic of language coupled with mathematical probability. We have seen that classical logic alone is insufficient to determine accurate diagnoses; hence, a conceptual and formal overview is given on why probability can be very useful. Providing illustrations of instances of clinical cases, we will see how the logic of probabilistic language is able to provide us a differential diagnosis in a ‘good enough’ way.

The conclusion is that it is possible to demonstrate that, even with the addition of probabilistic reasoning alone, it is not possible to determine exact diagnoses, so other enrichments are being sought for our language.