update chapter 2 and index

chapter2.qmd

@@ -112,31 +112,48 @@ computed by aggregating the errors across the entire validation data set
 
 ### Machine Learning in the Clinical Laboratory
 
-<!--# Can I copy this table? -->
+<!--# Table needs to be modified -->
 
-| **Author and Year** | **Objective and Machine Learning Task**                               | **Best Model** | **Major Themes** |
+| **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       |
+| 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       |
-Cao (2012) \|
+| Yang (2013)         | Predict normal reference ranges of ESR for various laboratories based on geographic and other clinical features | Neural Network | Interpretation   |
-
-                    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    |
 
 : Table 1. Summary of characteristics of machine learning algorithms
 [@rabbani2022].
 
-### 
+<!--# Need to fill in this section -->
 
-#### 
+## Reflex Testing
+
+The laboratory diagnosis of thyroid dysfunction relies on the
+measurement of circulating concentrations of thyrotropin (TSH), free
+thyroxine (fT4), and, in some cases, free triiodothyronine (fT3). TSH
+measurement is generally regarded as the most sensitive initial
+laboratory test for screening individuals for thyroid hormone
+abnormalities [@woodmansee2018]. TSH and fT4 have a complex, nonlinear
+relationship, such that small changes in fT4 result in relatively large
+changes in TSH [@plebani2020]. Many clinicians and laboratories check
+TSH alone as the initial test for thyroid problems and then only add a
+Free T4 measurement if the TSH is abnormal (outside the laboratory
+normal reference range), this is known as reflex testing
+[@woodmansee2018]. Reflex testing became possible with the advent of
+laboratory information systems (LIS) that were sufficiently flexible to
+permit modification of existing test requests at various stages of the
+analytical process [@srivastava2010]. Reflex testing is widely used, the
+major aim being to optimize the use of laboratory tests. However the
+common practice of reflex testing relies simply on hard coded rules that
+allow no flexibility. For instance in the case of TSH, free T4 will be
+added to the patient order whenever the value falls outside of the
+established laboratory reference range. This bring into the fold the
+issue that the thresholds used to trigger reflex addition of tests vary
+widely. In a study by Murphy he found the hypocalcaemic threshold to
+trigger magnesium measurement varied from 1.50 mmol/L up to 2.20 mmol/L
+[-@murphy2021]. Even allowing for differences in the nature, size and
+staffing of hospital laboratories, and populations served, the extent of
+the observed variation invites scrutiny [@murphy2021].
+
+<!--# insert table and study from strivastava about hypo/hyper thyroid -->
+
+<!--# data from woodmansee and plebani -->

index.qmd

@@ -20,16 +20,11 @@ existing test requests at various stages of the analytical process
 testing, those tests added automatically by a set of rules established
 in each laboratory. In most current clinical laboratories, reflex
 testing is performed with a 'hard' cutoff, using a specifically
-established range with no means of flexibility [@murphy2021].
+established range with no means of flexibility [@murphy2021]. This study
-
+will examine the use of Machine learning to develop algorithms to allow
-<!--# Rewrite this section -->
+flexibility for automatic reflex testing in clinical chemistry. The goal
-
+is to fill the gap between hard coded reflex testing and fully manual
-This study will examine the use of Machine learning to develop
+reflective testing using machine learning algorithms.
-algorithms to allow flexibility for automatic reflex testing in clinical
-chemistry. The goal is to fill the gap between hard coded reflex testing
-and fully manual reflective testing using machine learning algorithms.
-
-<!--#  -->
 
 ## Statement of Problem
 
@@ -77,15 +72,8 @@ widened to decrease the number of unnecessary Free T4 measurements
 performed. This reduction would reduce overall costs to the medical
 system without likely causing negative consequences of missing the
 detection of people with thyroid hormone abnormalities
-[@whitneyw.woodmansee2018].
+[@woodmansee2018]. Even with the potential reduction in testing the
-
+hard-coded reflex rule still exists.
-<!--# This paragraph should be written and most removed-->
-
-The reduction in testing aside, the hard-coded rule still exists.
-Additionally, machine learning may predict missing values in a patient's
-record or even suggest further testing on a particular patient. In a
-study at Massachusetts General hospital, researchers predicted ferritin
-results based on already run laboratory testing [@charnaalbert2020].
 
 ## Purposed Study Set Up
 

references.bib

@@ -214,3 +214,17 @@ PMID: 33045173}
 	doi = {10.1214/ss/1009213726},
 	url = {http://dx.doi.org/10.1214/ss/1009213726}
 }
+
+@article{woodmansee2018,
+	title = {Determination of optimal TSH ranges for reflex Free T4 testing},
+	author = {Woodmansee, Whitney W.},
+	year = {2018},
+	month = {02},
+	date = {2018-02},
+	journal = {Clinical Thyroidology for the Public},
+	pages = {3--4},
+	volume = {11},
+	number = {2},
+	url = {https://www.thyroid.org/patient-thyroid-information/ct-for-patients/february-2018/vol-11-issue-2-p-3-4/},
+	langid = {canadian}
+}