Update hybrid_diffeq.md

Author: ChrisRackauckas

docs/src/examples/hybrid_diffeq.md

@@ -71,9 +71,8 @@ Flux.train!(loss_n_ode, ps, data, ADAM(0.05), cb = cba)
 
 ## Note on Sensitivity Methods
 
-Current implementation of adjoint sensitivities are not compatible with callbacks
-that effect the state `u`, or are implicitly triggered with respect to `u`
-(i.e. a ContinuousCallback whose `condition` uses `u`). For these types of equations,
-methods based on discrete sensitivity analysis via automatic differentiation, like
-`ReverseDiffAdjoint`, `TrackerAdjoint`, or `ForwardDiffAdjoint` are the methods
-to use (and `ReverseDiffAdjoint` is demonstrated above).
+Only some continuous adjoint sensitivities are compatible with callbacks, namely
+`BacksolveAdjoint` and `InterpolatingAdjoint`. All methods based on discrete sensitivity 
+analysis via automatic differentiation, like `ReverseDiffAdjoint`, `TrackerAdjoint`, or 
+`ForwardDiffAdjoint` are the methods to use (and `ReverseDiffAdjoint` is demonstrated above),
+are compatible with events. This applies to SDEs, DAEs, and DDEs as well.