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The author is nearly there, but they are lacking a few key pieces in their ontology and thus their language traps them by forcing them to conflate concepts that

by tgbugs 6y ago

The author is nearly there, but they are lacking a few key pieces in their ontology and thus their language traps them by forcing them to conflate concepts that in fact need to be decoupled. The required concepts are as follows 1. condition or underlying biological etiology 2. diagnostic codes 3. symptoms 4. the patient 5. the person assigning the code to a patient 6. the impact of the symptoms on patient quality of life (this one is nearly always left out, I left it out before I noted as I was wrapping up the post) There are a number of relationships between these that I won't go into in detail, but to give one example, there is "some underlying condition may give rise to some symptoms." The problem the author complains about is an inevitable artifact cause by discretization of underlying continuous variables into discrete categories. This is a direct consequence of signal detection theory and is why you see things described as spectrum disorders, because diagnostic codes are applied to sets of symptoms that are produced by multiple different underlying etiologies, including underlying conditions which would fall into the category of "normal" in the population. This is exacerbated by the fact that there is significant variability in how the person assigning the codes assesses the symptoms. An example example of this is that medical professionals consistently underestimate the amount of pain experienced by black Americans [1], and this is pain which is about is unambiguous as you can get. The question then becomes, can we measure the various biases in these systems to try to recover what is really going on. Usually the answer is no. In the case of Alzheimer's disease post mortem pathology is absolutely required in order for a diagnosis to be confirmed, and we don't do enough autopsies to improve our models for mapping behavioral diagnosis to the pathology (which is definitional). This situation may improve over time, but as long as we cannot directly measure underlying etiology, those who may or may not suffer from an underlying condition will continue to be subjected to the current, clearly inadequate, system. 1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4843483/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4843483/

pmayrgundter 6y ago
Right on. Without etiology you have no mechanism and thus no hypothesis. This leaves unsolved the ambiguity between correlation and causation. But also agree (I think?) that it is pretty good otherwise. I didn't know that taxometrics existed as a field :-o
zby 6y ago
You suggest the author is conflating something - but you never explain what it is exactly. You just spill some general theory. You also write "The problem the author complains about is an inevitable artifact cause by discretization of underlying continuous variables into discrete categories" - but this is mistaken. There are underlying variables that in any practical interpretation are discrete - you can have flu or not - there is no spectrum. And the problem the author complains about is that we don't know which underlying variables are like having flu and which are like height.
- tgbugs 6y ago
  Having received a flu shot is a discrete measure. What we care about at the individual level is their response to that shot, which is continuous, minimally something like P(got-flu | got-flu-shot). What we care about at the population level is the probability that someone actually got the shot given that the records says that they did and the probability that they did get a flu shot given that the records say they didn't or that the records are absent. The conceptual data model for vaccination in the US is a table with ~370 million rows and a column for each vaccine with the type being the date the individual last received that vaccine or a null. It is nearly impossible to construct this table with any reasonable level of data quality. Even measurements of height are subject to error. The question is whether the error is larger than the level required for some particular use case. Thus it is not that height is somehow more discrete (height varies over the course of the day due to gravity), it is that our uses for it aren't affected by the very low uncertainty that its measurements entail. However, if you are building a spacesuit for someone and use a measurement of height taken at the end of the day in a gravitation field, they will be extremely uncomfortable when they get taller in space. Thus even when there are measurements which are nominally discrete, if the uncertainty is greater than that of the use case, then the fact that it is nominally discrete doesn't matter.
  - zby 6y ago
    The question the article is about is 'can we deduce that a condition is discrete when we cannot measure it directly, only via symptoms'. If we could measure it directly - than we would obviously get a discrete distribution - but lets assume we we cannot do it. For example lets assume that we don't have the hi-tech flu tests and we can only measure temperature, frequency of cough, and some more subjective stuff like muscle and body aches, sore throat etc. These symptoms are not discrete - if we measure them in the whole population we shell not get any discrete distribution. So the fact that we can still deduce that there is a discrete condition underlying these continuos distributions is surprising - but apparently there are methods for that and one of them is called MAXCOV and it is briefly described in the article. Also - what is the author conflating - can you explain what you mean by that?

by tgbugs 6y ago

pmayrgundter 6y ago

Right on. Without etiology you have no mechanism and thus no hypothesis. This leaves unsolved the ambiguity between correlation and causation. But also agree (I think?) that it is pretty good otherwise. I didn't know that taxometrics existed as a field :-o

zby 6y ago

You suggest the author is conflating something - but you never explain what it is exactly. You just spill some general theory. You also write "The problem the author complains about is an inevitable artifact cause by discretization of underlying continuous variables into discrete categories" - but this is mistaken. There are underlying variables that in any practical interpretation are discrete - you can have flu or not - there is no spectrum. And the problem the author complains about is that we don't know which underlying variables are like having flu and which are like height.

tgbugs 6y ago
Having received a flu shot is a discrete measure. What we care about at the individual level is their response to that shot, which is continuous, minimally something like P(got-flu | got-flu-shot). What we care about at the population level is the probability that someone actually got the shot given that the records says that they did and the probability that they did get a flu shot given that the records say they didn't or that the records are absent. The conceptual data model for vaccination in the US is a table with ~370 million rows and a column for each vaccine with the type being the date the individual last received that vaccine or a null. It is nearly impossible to construct this table with any reasonable level of data quality. Even measurements of height are subject to error. The question is whether the error is larger than the level required for some particular use case. Thus it is not that height is somehow more discrete (height varies over the course of the day due to gravity), it is that our uses for it aren't affected by the very low uncertainty that its measurements entail. However, if you are building a spacesuit for someone and use a measurement of height taken at the end of the day in a gravitation field, they will be extremely uncomfortable when they get taller in space. Thus even when there are measurements which are nominally discrete, if the uncertainty is greater than that of the use case, then the fact that it is nominally discrete doesn't matter.
- zby 6y ago
  The question the article is about is 'can we deduce that a condition is discrete when we cannot measure it directly, only via symptoms'. If we could measure it directly - than we would obviously get a discrete distribution - but lets assume we we cannot do it. For example lets assume that we don't have the hi-tech flu tests and we can only measure temperature, frequency of cough, and some more subjective stuff like muscle and body aches, sore throat etc. These symptoms are not discrete - if we measure them in the whole population we shell not get any discrete distribution. So the fact that we can still deduce that there is a discrete condition underlying these continuos distributions is surprising - but apparently there are methods for that and one of them is called MAXCOV and it is briefly described in the article. Also - what is the author conflating - can you explain what you mean by that?