diff --git a/R/collocationAnalysis.R b/R/collocationAnalysis.R
index 7240eca..41eb8d2 100644
--- a/R/collocationAnalysis.R
+++ b/R/collocationAnalysis.R
@@ -60,6 +60,7 @@
 #'   \item Ranks per label/measure with the pattern \code{rank_<label>_<measure>} (1 is best) and the corresponding percentile ranks \code{percentile_rank_<label>_<measure>}.
 #'   \item Pairwise contrasts for two-label comparisons, e.g. \code{delta_<measure>}, \code{delta_rank_<measure>}, and \code{delta_percentile_rank_<measure>}.
 #'   \item Summary columns describing the strongest labels per measure (\code{winner_*}, \code{runner_up_*}, \code{loser_*}, and \code{max_delta_*}), including winner/loser \code{webUIRequestUrl} columns. In multi-VC comparisons, missing per-label concordance URLs are derived from another available row URL for the same \code{node}/\code{collocate} by replacing the \code{cq} parameter with the target label's virtual corpus. Unsuffixed \code{winner_webUIRequestUrl} and \code{loser_webUIRequestUrl} columns are populated only when the score-based URL choices agree.
+#'   \item \code{imputed_<label>}, \code{n_imputed}, and \code{imputed}: flags marking rows whose scores were not observed for some label but imputed (see \code{missingScoreQuantile}). Filter with \code{dplyr::filter(!imputed)} to keep only collocates attested in every compared virtual corpus.
 #'   \item Optional helper columns such as \code{query}, \code{example}, or \code{url} when example retrieval is requested.
 #' }
 #' @importFrom dplyr arrange desc slice_head bind_rows group_by mutate ungroup left_join select row_number all_of first
@@ -726,6 +727,18 @@
 
   comparison <- dplyr::left_join(comparison, rank_data, by = c("node", "collocate"))
 
+  # Record which label/measure cells are absent *before* any imputation happens below.
+  # Deltas computed from imputed cells reflect presence/absence of the collocate in a
+  # virtual corpus, not a measured contrast, so users need to be able to tell them apart.
+  imputed_flags <- lapply(labels, function(safe_label) {
+    label_score_cols <- intersect(paste0(score_cols, "_", safe_label), names(comparison))
+    if (length(label_score_cols) == 0) {
+      return(rep(FALSE, nrow(comparison)))
+    }
+    Reduce(`|`, lapply(label_score_cols, function(col) is.na(comparison[[col]])))
+  })
+  names(imputed_flags) <- paste0("imputed_", labels)
+
   rank_replacements <- numeric(0)
   rank_column_names <- grep("^rank_", names(comparison), value = TRUE)
   if (length(rank_column_names) > 0) {
@@ -1230,6 +1243,16 @@
     comparison[[max_delta_pct_col]] <- max_deltas
   }
 
+  for (flag_col in names(imputed_flags)) {
+    comparison[[flag_col]] <- imputed_flags[[flag_col]]
+  }
+  if (length(imputed_flags) > 0) {
+    comparison$n_imputed <- as.integer(Reduce(`+`, lapply(imputed_flags, as.integer)))
+  } else {
+    comparison$n_imputed <- rep(0L, nrow(comparison))
+  }
+  comparison$imputed <- comparison$n_imputed > 0L
+
   collapse_consensus_url_columns <- function(url_cols) {
     if (length(url_cols) == 0) {
       return(rep(NA_character_, nrow(comparison)))
diff --git a/man/collocationAnalysis-KorAPConnection-method.Rd b/man/collocationAnalysis-KorAPConnection-method.Rd
index 8a3c899..e7875f4 100644
--- a/man/collocationAnalysis-KorAPConnection-method.Rd
+++ b/man/collocationAnalysis-KorAPConnection-method.Rd
@@ -99,6 +99,7 @@
 \item Ranks per label/measure with the pattern \code{rank_<label>_<measure>} (1 is best) and the corresponding percentile ranks \code{percentile_rank_<label>_<measure>}.
 \item Pairwise contrasts for two-label comparisons, e.g. \code{delta_<measure>}, \code{delta_rank_<measure>}, and \code{delta_percentile_rank_<measure>}.
 \item Summary columns describing the strongest labels per measure (\code{winner_*}, \code{runner_up_*}, \code{loser_*}, and \code{max_delta_*}), including winner/loser \code{webUIRequestUrl} columns. In multi-VC comparisons, missing per-label concordance URLs are derived from another available row URL for the same \code{node}/\code{collocate} by replacing the \code{cq} parameter with the target label's virtual corpus. Unsuffixed \code{winner_webUIRequestUrl} and \code{loser_webUIRequestUrl} columns are populated only when the score-based URL choices agree.
+\item \code{imputed_<label>}, \code{n_imputed}, and \code{imputed}: flags marking rows whose scores were not observed for some label but imputed (see \code{missingScoreQuantile}). Filter with \code{dplyr::filter(!imputed)} to keep only collocates attested in every compared virtual corpus.
 \item Optional helper columns such as \code{query}, \code{example}, or \code{url} when example retrieval is requested.
 }
 }
diff --git a/tests/testthat/test-collocations.R b/tests/testthat/test-collocations.R
index 94bc453..183de99 100644
--- a/tests/testthat/test-collocations.R
+++ b/tests/testthat/test-collocations.R
@@ -322,6 +322,81 @@
   )
 })
 
+test_that("add_multi_vc_comparisons flags imputed cells", {
+  sample_result <- tibble::tibble(
+    node = c("n", "n", "n"),
+    collocate = c("c1", "c2", "c2"),
+    vc = c("corpusSigle=/A/", "corpusSigle=/A/", "corpusSigle=/B/"),
+    label = c("A", "A", "B"),
+    N = c(100, 100, 100),
+    O = c(10, 10, 20),
+    O1 = c(50, 50, 50),
+    O2 = c(30, 30, 30),
+    E = c(5, 5, 5),
+    w = c(2, 2, 2),
+    leftContextSize = c(1, 1, 1),
+    rightContextSize = c(1, 1, 1),
+    frequency = c(10, 10, 20),
+    webUIRequestUrl = c(
+      "https://korap.example/A1",
+      "https://korap.example/A2",
+      "https://korap.example/B2"
+    ),
+    logDice = c(6, 5, 7),
+    pmi = c(3, 2, 4)
+  )
+
+  enriched <- RKorAPClient:::add_multi_vc_comparisons(sample_result)
+
+  expect_true(all(c("imputed_A", "imputed_B", "n_imputed", "imputed") %in% colnames(enriched)))
+
+  c1 <- enriched[enriched$collocate == "c1", ]
+  c2 <- enriched[enriched$collocate == "c2", ]
+
+  # c1 only occurs in A, so B's scores had to be imputed
+  expect_true(all(c1$imputed_B))
+  expect_true(all(!c1$imputed_A))
+  expect_true(all(c1$imputed))
+  expect_true(all(c1$n_imputed == 1L))
+
+  # c2 occurs in both, so nothing is imputed
+  expect_true(all(!c2$imputed_A))
+  expect_true(all(!c2$imputed_B))
+  expect_true(all(!c2$imputed))
+  expect_true(all(c2$n_imputed == 0L))
+
+  # c1's delta is measured against the imputed floor rather than against observed data:
+  # the absent label loses, at or below the weakest score actually attested anywhere.
+  expect_true(all(is.finite(c1$max_delta_logDice)))
+  expect_equal(unique(c1$loser_logDice), "B")
+  expect_lte(unique(c1$loser_logDice_value), min(sample_result$logDice))
+})
+
+test_that("add_multi_vc_comparisons reports no imputation when all labels are complete", {
+  sample_result <- tibble::tibble(
+    node = rep("n", 4),
+    collocate = c("c1", "c1", "c2", "c2"),
+    vc = rep(c("vc1", "vc2"), 2),
+    label = rep(c("A", "B"), 2),
+    N = rep(100, 4),
+    O = c(10, 20, 30, 40),
+    O1 = rep(50, 4),
+    O2 = rep(30, 4),
+    E = rep(5, 4),
+    w = rep(2, 4),
+    leftContextSize = rep(1, 4),
+    rightContextSize = rep(1, 4),
+    frequency = c(10, 20, 30, 40),
+    logDice = c(5, 7, 6, 4),
+    pmi = c(2, 3, 4, 1)
+  )
+
+  enriched <- RKorAPClient:::add_multi_vc_comparisons(sample_result)
+
+  expect_true(all(!enriched$imputed))
+  expect_true(all(enriched$n_imputed == 0L))
+})
+
 test_that("add_multi_vc_comparisons handles more than two labels", {
   sample_result <- tibble::tibble(
     node = rep("n", 3),