diff --git a/DESCRIPTION b/DESCRIPTION
index a342bd677..9035373e3 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -64,7 +64,7 @@ Config/Needs/website:
     r-lib/pkgdown,
     amirmasoudabdol/preferably
 Config/testthat/edition: 3
-RoxygenNote: 7.1.2
+RoxygenNote: 7.2.0
 URL: https://epiforecasts.io/scoringutils/, https://github.com/epiforecasts/scoringutils
 BugReports: https://github.com/epiforecasts/scoringutils/issues
 VignetteBuilder: knitr
diff --git a/R/score_quantile.R b/R/score_quantile.R
index 5616eb095..b0dda73f5 100644
--- a/R/score_quantile.R
+++ b/R/score_quantile.R
@@ -71,7 +71,7 @@ score_quantile <- function(data,
       list(true_value, lower,
         upper, range,
         weigh,
-        separate_results = TRUE
+        separate_results = separate_results
       )
     )]
   }
diff --git a/man/scoringutils-package.Rd b/man/scoringutils-package.Rd
index ef49fa8d4..540e57a3f 100644
--- a/man/scoringutils-package.Rd
+++ b/man/scoringutils-package.Rd
@@ -6,7 +6,7 @@
 \alias{scoringutils-package}
 \title{scoringutils: Utilities for Scoring and Assessing Predictions}
 \description{
-Provides a collection of metrics and proper scoring rules (Tilmann Gneiting & Adrian E Raftery (2007) <doi:10.1198/016214506000001437>, Jordan, A., Krüger, F., & Lerch, S. (2019) <doi:10.18637/jss.v090.i12>) within a consistent framework for evaluation, comparison and visualisation of forecasts. In addition to proper scoring rules, functions are provided to assess bias, sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019) <doi:10.1371/journal.pcbi.1006785>) of forecasts. Several types of predictions (e.g. binary, discrete, continuous) which may come in different formats (e.g. forecasts represented by predictive samples or by quantiles of the predictive distribution) can be evaluated. Scoring metrics can be used either through a convenient data.frame format, or can be applied as individual functions in a vector / matrix format. All functionality has been implemented with a focus on performance and is robustly tested.
+Provides a collection of metrics and proper scoring rules (Tilmann Gneiting & Adrian E Raftery (2007) \doi{10.1198/016214506000001437}, Jordan, A., Krüger, F., & Lerch, S. (2019) \doi{10.18637/jss.v090.i12}) within a consistent framework for evaluation, comparison and visualisation of forecasts. In addition to proper scoring rules, functions are provided to assess bias, sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019) \doi{10.1371/journal.pcbi.1006785}) of forecasts. Several types of predictions (e.g. binary, discrete, continuous) which may come in different formats (e.g. forecasts represented by predictive samples or by quantiles of the predictive distribution) can be evaluated. Scoring metrics can be used either through a convenient data.frame format, or can be applied as individual functions in a vector / matrix format. All functionality has been implemented with a focus on performance and is robustly tested.
 }
 \seealso{
 Useful links:
diff --git a/tests/testthat/test-score.R b/tests/testthat/test-score.R
index 8f647248b..3b42c8e63 100644
--- a/tests/testthat/test-score.R
+++ b/tests/testthat/test-score.R
@@ -60,6 +60,22 @@ test_that("function produces output for a quantile format case", {
   )
 })
 
+test_that("score_quantile correctly handles separate results = FALSE", {
+  quantile_example <- data.table::setDT(scoringutils::example_quantile)
+  eval <- suppressMessages(
+    score(
+      quantile_example[!is.na(prediction)],
+      separate_results = FALSE
+    )
+  )
+
+  expect_equal(
+    nrow(eval) > 1,
+    TRUE
+  )
+})
+
+
 test_that("score() quantile produces desired metrics", {
   data <- data.frame(
     true_value = rep(1:10, each = 2),