 	#   {
 	#     "doc_id" : 549dae9e0a2a615c0c7f0c98,
 	#     "project_id" : 549dae9c0a2a615c0c7f0c8c,
-	#     "pack" : [ D1, D2, D3, ....],
+	#     "pack" : [ U1, U2, U3, ...., UN],
 	#     "meta" : {	"user_id" : 52933..., "start_ts" : 1422310693931,	"end_ts" : 1422310693931 },
 	#     "v" : 17082
 	#   }
 	#
-	#  where D1, D2, D3, .... are single updates stripped of their
+	#  where U1, U2, U3, .... are single updates stripped of their
 	#  doc_id and project_id fields (which are the same for all the
-	#  updates in the pack).  The meta and v fields of the pack itself
-	#  are those of the first entry in the pack D1 (this makes it
-	#  possible to treat packs and single updates in the same way).
-
+	#  updates in the pack).
+	#
+	#  The pack itself has v and meta fields, this makes it possible to
+	#  treat packs and single updates in the same way.
+	#
+	#  The v field of the pack itself is from the first entry U1
+	#  The meta.end_ts field of the pack itself is from the last entry UN.
 
 	findDocResults: (collection, query, limit, callback) ->
 		# query - the mongo query selector, includes both the doc_id/project_id and

			tail = collection
				.find(tailQuery, projection)
				.sort(sort)

			#console.log 'tailQuery is', tailQuery

			# now find any packs that overlap with the time window
			overlapQuery = _.clone(query)

				.find(overlapQuery, projection)
				.sort(sort)

			# we don't specify a limit here, as there could be any number of overlaps
			# NB. need to catch items in original query and followup query for duplicates

