# Discussion

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## Summary of Results

## Study Limitations

Section overview - In progress

### MIMIC Database

While the MIMIC-IV database allowed for a first run of the study, it
does suffer from some issues compared to other patient results. The
MIMIC-IV database only contains results from ICU patients. Thus the
result may not represent normal results for patients typically screened
for hyper or hypothyroidism. In a study by Tyler et al., they found that
laboratory value ranges from critically ill patients deviate
significantly from those of healthy controls [-@tyler2018]. In their
study, distribution curves based on ICU data differed significantly from
the hospital standard range (mean \[SD\] overlapping coefficient, 0.51
\[0.32-0.69\]) [@tyler2018]. The data ranges from 2008 to 2019. During
this time, there could have been several unknown laboratory changes.
Often laboratories change methods, reference ranges, or even vendors.
None of this data is available in the MIMIC database. A change in method
or vendor could cause a shift in results, thus causing the algorithm to
assign incorrect outcomes.

The dataset also sufferers from incompleteness. Due to the fact the
database was not explicitly designed for this study, many patients do
not have complete sets of lab results. The study also had to pick and
choose lab tests to allow for as many sets of TSH and Free T4 results as
possible. For instance, in a study by Luo et al., a total of 42
different lab tests were selected for a Machine Learning study, compared
to only 16 selected for this study [-@luo2016]. The patient demographic
data also suffered from the same incompleteness. Due to this fact, only
the age and gender of the patient were used in developing the algorithm.
An early study by Schectman et al. found the mean TSH level of Blacks
was 0.4 (SE .053) mU/L lower than that for Whites after age and sex
adjustment, race explaining 6.5 percent of the variation in TSH levels
[-@schectman1991]. This variation in results should potentially be
included in developing a future algorithm. However, as it stands, the
current data set has incomplete data for patient race and ethnicity.

## Real World Applications 
