diff --git a/chapter2.qmd b/chapter2.qmd
index 39203fb..bbe7bf7 100644
--- a/chapter2.qmd
+++ b/chapter2.qmd
@@ -211,14 +211,14 @@ tree-based learning algorithms and neural networks often performed best.
 @tbl-lab_ml displays the overview of their research.
 
 | **Author and Year** | **Objective and Machine Learning Task**                                                                                | **Best Model**        | **Major Themes**                       |
-|:-----------------|:-----------------|:-----------------|:-----------------|
+|:--------------------|:-----------------------------------------------------------------------------------------------------------------------|:----------------------|:---------------------------------------|
 | Azarkhish (2012)    | Predict iron deficiency anemia and serum iron levels from CBC indices                                                  | Neural Network        | Prediction                             |
 | Cao (2012)          | Triage manual review for urinalysis samples                                                                            | Tree-based            | Automation                             |
 | Yang (2013)         | Predict normal reference ranges of ESR for various laboratories based on geographic and other clinical features        | Neural Network        | Interpretation                         |
 | Lidbury (2015)      | Predict liver function test results from other tests in the panel, highlighting redundancy in the liver function panel | Tree-based            | Prediction, Utilization                |
 | Demirci (2016)      | Classify whether critical lab result is valid or invalid using other lab values and clinical information               | Neural Network        | Automation, Interpretation, Validation |
 | Luo (2016)          | Predict ferritin from other tests in iron panel                                                                        | Tree-based            | Prediction, Utilization                |
-| Poole (2016)        | Create personalized reference ranges that take into account patients\' diagnoses                                       | Unsupervised learning | Interpretation                         |
+| Poole (2016)        | Create personalized reference ranges that take into account patients' diagnoses                                        | Unsupervised learning | Interpretation                         |
 | Parr (2018)         | Automate mapping of Veterans Affair laboratory data to LOINC codes                                                     | Tree-based            | Information systems, Automation        |
 | Wilkes (2018)       | Classify urine steroid profiles as normal or abnormal, and further interpret into specific disease processes           | Tree-based            | Interpretation, Automation             |
 | Fillmore (2019)     | Automate mapping of Veterans Affair laboratory data to LOINC codes                                                     | Tree-based            | Information systems, Automation        |
diff --git a/chapter5.qmd b/chapter5.qmd
index c7528f1..78247c2 100644
--- a/chapter5.qmd
+++ b/chapter5.qmd
@@ -15,11 +15,12 @@ the algorithm had a false positive rate of 8% and a false negative rate
 of 20%. In the original data, 67% of the time, the result was
 non-diagnostic for Hyper-Thryodism.
 
-1846 - Hypo
-
-423 - Hyper
-
-9170 - High TSH (hypo)
+While TSH was expected to be the most important variable in building
+random forest models, it was quite unexpected that the next three values
+would be Hematology results. In the clinical laboratory, TSH and CBCs
+are often run on different analyzers and often in completely different
+departments. Finding this slight correlation could be valuable to
+building further algorithms.
 
 ## Real World Applications 
 
@@ -49,6 +50,13 @@ informationally redundant testing [@luo2016]. However, since Free T4 and
 all other tests used in this study are performed on automated
 instruments, the cost savings to the lab and patient may be minimal.
 
+As Rabbani et al. study showed, Machine Learning in the Clinical
+Laboratory is an emerging field. However, few existing studies relate to
+predicting laboratory values based on other results [-@rabbani2022]. The
+few studies that do exist follow a similar premise. All are trying to
+reduce redundant laboratory testing and thus lower the cost burden on
+the patient.
+
 ## Study Limitations
 
 Section overview - In progress