diff --git a/LegendBasicViewer/PlotsAndTables.R b/LegendBasicViewer/PlotsAndTables.R
index 96526987..4b036e22 100644
--- a/LegendBasicViewer/PlotsAndTables.R
+++ b/LegendBasicViewer/PlotsAndTables.R
@@ -6,9 +6,9 @@ createTitle <- function(tcoDbs) {
 
   titles <- paste(tcoDbs$outcomeName,
                   "risk in new-users of",
-                  uncapitalize(tcoDbs$targetName),
+                  tcoDbs$targetName,
                   "versus",
-                  uncapitalize(tcoDbs$comparatorName),
+                  tcoDbs$comparatorName,
                   "for",
                   uncapitalize(tcoDbs$indicationId),
                   "in the",
@@ -108,7 +108,7 @@ prepareFollowUpDistTable <- function(followUpDist) {
 
 prepareMainResultsTable <- function(mainResults, analyses) {
   table <- mainResults
-  table$hr <- sprintf("%.2f (%.2f - %.2f)", mainResults$rr, mainResults$ci95lb, mainResults$ci95ub)
+  table$hr <- sprintf("%.2f (%.2f - %.2f)", mainResults$rr, mainResults$ci95Lb, mainResults$ci95Ub)
   table$p <- sprintf("%.2f", table$p)
   table$calHr <- sprintf("%.2f (%.2f - %.2f)",
                          mainResults$calibratedRr,
@@ -352,6 +352,7 @@ plotPs <- function(ps, targetName, comparatorName) {
   ps <- rbind(data.frame(x = ps$preferenceScore, y = ps$targetDensity, group = targetName),
               data.frame(x = ps$preferenceScore, y = ps$comparatorDensity, group = comparatorName))
   ps$group <- factor(ps$group, levels = c(as.character(targetName), as.character(comparatorName)))
+  levels(ps$group) <- paste0(" " , levels(ps$group), " ") # Add space between legend labels
   theme <- ggplot2::element_text(colour = "#000000", size = 12)
   plot <- ggplot2::ggplot(ps,
                           ggplot2::aes(x = x, y = y, color = group, group = group, fill = group)) +
@@ -506,24 +507,24 @@ getCoverage <- function(controlResults) {
   d <- rbind(data.frame(yGroup = "Uncalibrated",
                         logRr = controlResults$logRr,
                         seLogRr = controlResults$seLogRr,
-                        ci95lb = controlResults$ci95lb,
-                        ci95ub = controlResults$ci95ub,
+                        ci95Lb = controlResults$ci95Lb,
+                        ci95Ub = controlResults$ci95Ub,
                         trueRr = controlResults$effectSize),
              data.frame(yGroup = "Calibrated",
                         logRr = controlResults$calibratedLogRr,
                         seLogRr = controlResults$calibratedSeLogRr,
-                        ci95lb = controlResults$calibratedCi95Lb,
-                        ci95ub = controlResults$calibratedCi95Ub,
+                        ci95Lb = controlResults$calibratedCi95Lb,
+                        ci95Ub = controlResults$calibratedCi95Ub,
                         trueRr = controlResults$effectSize))
   d <- d[!is.na(d$logRr), ]
-  d <- d[!is.na(d$ci95lb), ]
-  d <- d[!is.na(d$ci95ub), ]
+  d <- d[!is.na(d$ci95Lb), ]
+  d <- d[!is.na(d$ci95Ub), ]
   if (nrow(d) == 0) {
     return(NULL)
   }
 
   d$Group <- as.factor(d$trueRr)
-  d$Significant <- d$ci95lb > d$trueRr | d$ci95ub < d$trueRr
+  d$Significant <- d$ci95Lb > d$trueRr | d$ci95Ub < d$trueRr
 
   temp2 <- aggregate(Significant ~ Group + yGroup, data = d, mean)
   temp2$coverage <- (1 - temp2$Significant)
@@ -537,23 +538,23 @@ plotScatter <- function(controlResults) {
   d <- rbind(data.frame(yGroup = "Uncalibrated",
                         logRr = controlResults$logRr,
                         seLogRr = controlResults$seLogRr,
-                        ci95lb = controlResults$ci95lb,
-                        ci95ub = controlResults$ci95ub,
+                        ci95Lb = controlResults$ci95Lb,
+                        ci95Ub = controlResults$ci95Ub,
                         trueRr = controlResults$effectSize),
              data.frame(yGroup = "Calibrated",
                         logRr = controlResults$calibratedLogRr,
                         seLogRr = controlResults$calibratedSeLogRr,
-                        ci95lb = controlResults$calibratedCi95Lb,
-                        ci95ub = controlResults$calibratedCi95Ub,
+                        ci95Lb = controlResults$calibratedCi95Lb,
+                        ci95Ub = controlResults$calibratedCi95Ub,
                         trueRr = controlResults$effectSize))
   d <- d[!is.na(d$logRr), ]
-  d <- d[!is.na(d$ci95lb), ]
-  d <- d[!is.na(d$ci95ub), ]
+  d <- d[!is.na(d$ci95Lb), ]
+  d <- d[!is.na(d$ci95Ub), ]
   if (nrow(d) == 0) {
     return(NULL)
   }
   d$Group <- as.factor(d$trueRr)
-  d$Significant <- d$ci95lb > d$trueRr | d$ci95ub < d$trueRr
+  d$Significant <- d$ci95Lb > d$trueRr | d$ci95Ub < d$trueRr
   temp1 <- aggregate(Significant ~ Group + yGroup, data = d, length)
   temp2 <- aggregate(Significant ~ Group + yGroup, data = d, mean)
   temp1$nLabel <- paste0(formatC(temp1$Significant, big.mark = ","), " estimates")
@@ -630,7 +631,7 @@ plotScatter <- function(controlResults) {
 }
 
 plotLargeScatter <- function(d, xLabel) {
-  d$Significant <- d$ci95lb > 1 | d$ci95ub < 1
+  d$Significant <- d$ci95Lb > 1 | d$ci95Ub < 1
   
   oneRow <- data.frame(nLabel = paste0(formatC(nrow(d), big.mark = ","), " estimates"),
                        meanLabel = paste0(formatC(100 *
@@ -871,10 +872,19 @@ judgePropensityScore <- function(ps, bias) {
 }
 
 uncapitalize <- function(x) {
-  if (is.character(x)) {
-    substr(x, 1, 1) <- tolower(substr(x, 1, 1))
-  }
-  x
+  if (length(x) > 1) {
+    x <- x[1]
+  }
+  terms <- strsplit(x, split = " & ")
+  terms <- sapply(terms, FUN = function(y) {
+    substr(y, 1, 1) <- tolower(substr(y, 1, 1))
+    y <- gsub("aCE", "ACE", y)
+    y <- gsub("CCB)", "CCBs)", y)
+    y
+  })
+  result <- paste(terms, collapse = " and ")
+  names(result) <- NULL
+  result
 }
 
 capitalize <- function(x) {