Apply suggestions from code review

Co-authored-by: Christopher Rackauckas <accounts@chrisrackauckas.com>

Author: ChrisRackauckas

docs/src/basics/solution.md

@@ -151,7 +151,7 @@ The solution interface also includes some special fields. The problem object
 `prob` and the algorithm used to solve the problem `alg` are included in the
 solution. Additionally, the field `dense` is a boolean which states whether
 the interpolation functionality is available. Further, the field `stats`
-(formerly `destats`, renamed in SciMLBase v3) contains the internal statistics
+contains the internal statistics
 for the solution process, such as the number of linear solves and convergence
 failures. Lastly, there is a mutable state `tslocation` which controls the
 plot recipe behavior. By default, `tslocation=0`.

docs/src/features/io.md

@@ -21,9 +21,7 @@ df = DataFrames.DataFrame(sol)
 ```
 
 If we attach an ModelingToolkit / SciML symbolic system via the problem's
-`sys`, then those names will be used (the old `syms`/`paramsyms`/`indepsym`
-kwargs on `ODEFunction` were removed in SciMLBase v3; use the symbolic
-interface instead):
+`sys`, then those names will be used:
 
 ```@example IO
 df = DataFrames.DataFrame(sol)

docs/src/features/linear_nonlinear.md

@@ -37,9 +37,7 @@ on the choices.
 ## Preconditioners: `precs` Specification
 
 Any [LinearSolve.jl-compatible preconditioner](https://docs.sciml.ai/LinearSolve/stable/basics/Preconditioners/)
-can be used as a left or right preconditioner. Starting with OrdinaryDiffEq
-v7 / DiffEqBase v7, the per-solver `precs` kwarg and the old `DEFAULT_PRECS`
-default were removed — preconditioners are now configured on the `linsolve`
+can be used as a left or right preconditioner. Preconditioners are configured on the `linsolve`
 object itself, through LinearSolve's `Pl` / `Pr` interface. For example:
 
 ```julia

docs/src/features/performance_overloads.md

@@ -17,9 +17,8 @@ of pre-computed functions to speed up the calculations. This is offered via the
 
 All applicable stiff differential equation solvers in the Julia ecosystem
 (OrdinaryDiffEq.jl, StochasticDiffEq.jl, DelayDiffEq.jl, etc.) take an
-`autodiff` keyword for handling automatic Jacobian construction. Starting with
-OrdinaryDiffEq v7 / SciMLBase v3 this takes an `ADTypes` object rather than a
-`Bool`, which means every AD backend in the Julia ecosystem is supported
+`autodiff` keyword for handling automatic Jacobian construction. This takes an `ADTypes` object, 
+which means every AD backend in the Julia ecosystem is supported
 through the same interface:
 
   - `autodiff`: Specifies the AD backend to use for Jacobian / gradient
@@ -34,10 +33,6 @@ through the same interface:
     `nothing`, which means it will be chosen true/false depending on circumstances
     of the solver, such as whether a Krylov subspace method is used for `linsolve`.
 
-The old solver kwargs `chunk_size`, `diff_type`, and `standardtag` were
-removed in OrdinaryDiffEq v7 — the first two are now configured on the
-`ADTypes` object (`AutoForwardDiff(chunksize=…)` /
-`AutoFiniteDiff(fdtype=…)`) and the latter is always on.
 
 ## Passing Jacobian Function Definitions
 

docs/src/solvers/ode_solve.md

@@ -207,8 +207,6 @@ These methods when lazy assume that the parameter vector `p` will be unchanged
 between the moment of the interval solving and the interpolation. If `p` is
 changed in a ContinuousCallback, or in a DiscreteCallback and the continuous
 solution is used after the full solution, then set `lazy=Val{false}()`.
-Starting with OrdinaryDiffEq v7 this kwarg must be a `Val{Bool}` rather than
-a raw `Bool`.
 
 Example:
 
@@ -806,9 +804,6 @@ alg = ExplicitRK(tableau = OrdinaryDiffEqExplicitTableaus.DormandPrince())
 solve(prob, alg)
 ```
 
-(The `construct*` prefix on the tableau constructors was dropped in
-OrdinaryDiffEq v7; the new names live in `OrdinaryDiffEqExplicitTableaus` /
-`OrdinaryDiffEqImplicitTableaus` and are no longer exported.)
 
 #### CompositeAlgorithm
 
@@ -1252,11 +1247,9 @@ import NeuralPDE
 
 ### List of Supplied Tableaus
 
-A large variety of tableaus have been supplied by default. Starting with
-OrdinaryDiffEq v7 / DiffEqDevTools v3 these live in
+A large variety of tableaus have been supplied by default. These live in
 `OrdinaryDiffEqExplicitTableaus` / `OrdinaryDiffEqImplicitTableaus` under
-their bare names (no `construct` prefix), and are no longer exported — call
-them qualified. The list of tableaus can be found in
+their bare names. The list of tableaus can be found in
 [the developer docs](https://docs.sciml.ai/DiffEqDevDocs/stable/internals/tableaus/).
 To use them:
 

docs/src/tutorials/advanced_ode_example.md

@@ -225,8 +225,7 @@ valid [LinearSolve.jl](https://docs.sciml.ai/LinearSolve/stable/) solver.
 ## Adding a Preconditioner
 
 Any [LinearSolve.jl-compatible preconditioner](https://docs.sciml.ai/LinearSolve/stable/basics/Preconditioners/)
-can be used as a preconditioner in the linear solver interface. Starting with
-OrdinaryDiffEq v7 / DiffEqBase v7, the `precs` keyword is on the
+can be used as a preconditioner in the linear solver interface. The `precs` keyword is on the
 LinearSolve.jl linear solver object (e.g. `KrylovJL_GMRES(precs = ...)`)
 rather than on the ODE algorithm, and the `precs` callback signature is
 `Pl, Pr = precs(A, p)` — where `A` is the current `W = I - γJ` operator and