GITNUXSOFTWARE REVIEW
Telecommunications ConnectivityTop 10 Best Forward Error Correction Software of 2026
Editorial roundup comparing forward error correction software tools like GNU Radio, Codec2, and Liquid DSP for engineers choosing transmission error handling.
How we ranked these tools
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Framework: Features 40% · Ease 30% · Value 30%
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Codec2 is the best pick for teams that need compact, reference-grade FEC code and controlled BER testing because it includes built-in channel-coding test workflows that measure BER across chosen parameter sets, while GNU Radio is the better alternative for integrating FEC blocks into a streaming SDR signal chain since decoding runs inside its schedulers alongside channel modeling.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Codec2
Built-in channel coding test workflows that measure BER outcomes for chosen code parameter sets.
Best for: Fits when teams need compact, reference-grade FEC code for controlled BER testing and SDR or embedded links.
Liquid DSP
Editor pickBit-accurate encode and decode harnesses that let error-rate measurements follow directly from controlled channel inputs.
Best for: Fits when research teams need reproducible FEC encoding and decoding runs with measurable error-rate results.
GNU Radio
Editor pickFEC blocks run inside GNU Radio streaming schedulers, letting decoding operate alongside real-time synchronization and channel modeling.
Best for: Fits when teams need physical-layer FEC integrated with modulation, channel simulation, and streaming SDR execution.
Comparison Table
Codec2
open-sourceOpen-source low-bitrate speech codec with forward error correction for digital voice communications.
Built-in channel coding test workflows that measure BER outcomes for chosen code parameter sets.
Codec2 provides forward error correction tooling focused on compact binary channel coding workflows for low-latency links. The project ships reference implementations and a buildable codebase for encoder and decoder pipelines that operate on real symbols rather than abstract math.
It also includes test harnesses and documentation that describe how to select code parameters and evaluate bit error behavior under channel models. Deployment typically targets embedded and SDR-adjacent environments where code rate choice and decoder latency matter.
- +Reference encoders and decoders come with practical channel test harnesses
- +Parameterized code settings support controlled experiments across code rates
- +Lean implementation style fits embedded and SDR integration needs
- +Clear build instructions make it usable as a starting point for custom links
- –Integration work is needed to embed Codec2 FEC into packet-oriented stacks
- –High-level orchestration and automation tooling is limited
- –Ecosystem integrations with mainstream comms frameworks are not provided
- –Decoder performance tuning requires source-level understanding
Best for: Fits when teams need compact, reference-grade FEC code for controlled BER testing and SDR or embedded links.
Liquid DSP
open-sourceC library of digital signal processing modules including FEC encoders and decoders for software-defined radio.
Bit-accurate encode and decode harnesses that let error-rate measurements follow directly from controlled channel inputs.
Liquid DSP is built around FEC experiments where users run repeatable encoding and decoding steps on captured or synthetic payloads. The toolchain supports multiple code families via separate modules and exposes parameters that affect redundancy overhead and decoder operation. Verification is done by comparing decoded bits against the original payload and by measuring failure rates at chosen operating points.
A key tradeoff is that Liquid DSP emphasizes engineering convenience for research-style runs rather than production-grade link integration. It fits best when FEC is implemented around custom transports where decoding latency and throughput tradeoffs can be measured end to end in the lab.
- +Configurable coding parameters for redundancy and decoder behavior tuning
- +Repeatable command-line style workflows for bit-accurate validation
- +Multiple FEC code families exposed as separate, runnable components
- +Channel-condition testing by driving the full encode to decode loop
- –No turnkey integration layer for standards-based physical-layer FEC
- –Decoder performance tuning requires familiarity with coding parameters
- –Limited governance controls for multi-tenant automation compared to enterprise stacks
- –Throughput optimization is not packaged as an end-to-end link pipeline
RF systems engineers
Evaluate decoder failure rates versus noise
BER and packet failure curves
Embedded protocol developers
Prototype link-layer FEC for packets
Lower packet error rate
Show 2 more scenarios
Data pipeline test teams
Create regression tests for recovery
Stable regression pass criteria
Use deterministic bit-level vectors and decoder runs to detect changes that increase decoding failures.
Modeling and simulation groups
Stress ECC under synthetic channels
Repeatable scenario reports
Drive controlled channel models into the full FEC loop and record the decoding outcomes per setting.
Best for: Fits when research teams need reproducible FEC encoding and decoding runs with measurable error-rate results.
GNU Radio
developer toolkitAn open-source signal-processing framework with channel coding and FEC blocks.
FEC blocks run inside GNU Radio streaming schedulers, letting decoding operate alongside real-time synchronization and channel modeling.
GNU Radio couples a graphical flowgraph workflow with C++ and Python blocks to build and test physical-layer signal chains end to end. Forward error correction support comes through code blocks and decoder logic that can run in real time over streaming sources like SDR devices or file inputs.
The main distinction is how FEC decoding is integrated into the same runtime that performs modulation, channel modeling, and synchronization. This design supports tight iteration loops for code choice, soft-decision versus hard-decision behavior, and decoder latency trade-offs.
- +Flowgraph ties FEC encode and decode to the full SDR signal chain
- +Python block development supports custom FEC decoders and channel models
- +Streaming execution enables measuring decoder latency under realistic throughput
- +C and C++ block APIs support performance-focused decoder implementations
- –FEC setups often require code-length tuning and careful stream alignment
- –Standards-grade interoperability needs extra work outside the core blocks
- –Large LDPC and turbo experiments may demand substantial compute resources
- –Operational tooling for governance and audit trails is minimal
Best for: Fits when teams need physical-layer FEC integrated with modulation, channel simulation, and streaming SDR execution.
MATLAB Communications Toolbox
enterpriseProvides channel coding, modulation, and error-control simulation functions for communications systems.
Iterative decoding tooling with per-iteration control for LDPC workflows and measurable convergence behavior.
MATLAB Communications Toolbox supports common FEC experiments with built-in encoders, decoders, and channel models, including soft-decision and iterative decoding flows. It pairs these blocks with measurement tooling for BER and packet-error style outcomes so code rate and block length tradeoffs can be compared under controlled channel conditions. The toolbox also fits automation needs through MATLAB scripting around block construction, parameter sweeps, and repeatable random seeds for deterministic runs. The result is strong integration depth for link-level development where code performance must be tied to simulation settings and timing assumptions.
- +Built-in LDPC and convolutional coding plus iterative decoding helpers
- +Scripting supports repeatable parameter sweeps for BER and PER-style evaluation
- +Soft-decision decoding paths enable realistic receiver performance studies
- +System object interfaces simplify integration into larger comms simulations
- –MATLAB-centric execution can complicate direct embedded deployment
- –Throughput-focused streaming optimization is limited compared to DSP-first stacks
- –Custom decoder integration still requires careful data-shaping across blocks
- –Large simulation graphs can slow down long Monte Carlo runs
Physical-layer researchers
Compare LDPC designs under fading channels
Faster link-margin decision cycles
Verification engineers
Regression-test decoder behavior on stored vectors
Repeatable decode verification
Show 2 more scenarios
Algorithm developers
Prototype custom channel coding algorithms
Short iteration time
Implement encoders and decoders in MATLAB and connect them into comms block simulations.
System engineers
Model end-to-end link throughput limits
Clear redundancy overhead tradeoffs
Combine FEC choices with channel impairment models and evaluate packet error outcomes.
Best for: Fits when teams prototype and validate FEC performance in MATLAB before porting selected decoders.
Rohde & Schwarz VSE
enterpriseVector signal explorer software with FEC analysis and decoding for 5G and DVB signal testing.
A verification-style execution workflow that ties coded payload handling and decoder configuration to measurement-oriented run control.
Rohde & Schwarz VSE differentiates itself by pairing forward error correction workflows with RF and baseband test instrumentation expectations, which shapes its end-to-end coding and decoding setup. The software supports channel coding for link-layer style experiments, including iterative and block-based FEC configurations and practical decoder execution parameters.
- +Test-centric configuration model aligned to RF-to-decoder experiments
- +Supports practical FEC parameter tuning for coding and decoder behavior
- +Designed for repeatable runs where impairments map to packet outcomes
- +Works well for verification-style workflows that need consistent outputs
- –Workflow-first UI can slow down scripted batch sweeps
- –Integration depth for external automation depends on available interfaces
- –Less suited for quick, code-only prototyping of new codes
- –Setup requires careful alignment of coding, framing, and decoder settings
Best for: Fits when link-level FEC validation needs repeatable decoder experiments integrated into RF-centric test workflows.
TurboConcept FEC IP Cores
enterpriseFEC encoder and decoder IP cores for 5G LDPC, 5G Polar, LTE turbo, DVB-RCS, CCSDS, and turbo product codes targeting FPGA and ASIC.
Hardware-first FEC IP integration with deterministic timing targets for physical-layer decoders.
TurboConcept FEC IP Cores provide deployable FPGA or ASIC forward error correction building blocks focused on physical-layer coding and decoding inside a hardware datapath. The differentiator is an IP-core oriented workflow that targets integration into modems and link layers that need deterministic decoder latency and throughput.
- +IP-core integration fits hardware datapaths with predictable timing
- +Configurable code-rate controls via puncturing-style parameterization
- +Supports iterative decoding workflows with soft-decision inputs
- +Designed for deterministic decoder latency targets
- –Primarily hardware integration, not software tooling for link simulation
- –Limited visibility into throughput tuning without deep RTL design context
- –Setup depends on lab-specific calibration for decoder metric scaling
- –Documentation depth can be sparse for end-to-end verification
Best for: Fits when teams need FPGA or ASIC FEC blocks with deterministic latency and tight physical-layer integration.
NVIDIA Sionna
API-firstAn open-source Python library for link-level communication system simulation and machine learning research.
Graph-structured coding simulation with soft information message passing for iterative decoder experiments.
Sionna is built for end-to-end link simulations where FEC performance is driven by channel statistics, modulation, and decoder behavior in one place. It includes channel abstractions and coding components that work with soft information flows, which is critical for comparing coding gain and error-rate trends across operating points. The tight coupling to the simulation runtime also reduces glue code when generating BER and PER curves for different code rates and block lengths.
- +Python-first coding and channel simulation keeps FEC experiments reproducible
- +Soft information flows support iterative decoding research workflows
- +Configurable components enable decoder-iteration sweeps and controlled comparisons
- +Tensor-based interfaces fit automated batch runs
- –Primarily a simulation tool rather than a production FEC integration layer
- –Custom code design can require familiarity with coding graph structures
- –Decoder latency realism depends on how the runtime maps to targets
- –Interoperability with external encoders can require custom wrappers
Physical-layer research engineers
Benchmark iterative decoder variants
Parameterized performance curves
Wireless systems teams
Co-design FEC with channel assumptions
Evidence-based code selection
Show 1 more scenario
ML-driven communications researchers
Integrate learned components
FEC experiment automation
Use tensor interfaces to combine coding blocks with custom model logic for experiments.
Best for: Fits when teams need controlled FEC experiments with soft-decision iterative decoding and batch automation.
AFF3CT
vertical specialistAn open-source simulator for channel coding and iterative decoding algorithms.
A configurable simulation harness that couples channel models to decoders for direct throughput and latency measurement.
AFF3CT compiles and runs forward error correction and coding algorithms for research and integration into communication stacks. The codebase includes modular encoders and decoders that support multiple code families and soft- and hard-decision decoding workflows.
- +Modular encoder and decoder components suitable for mixed coding experiments
- +Built-in simulation harness with measurable latency and error-rate outputs
- +Supports soft-decision decoding pipelines for iterative decoders and LDPC workflows
- +Deterministic configuration via code parameters for repeatable benchmark runs
- –Workflow configuration relies on developer-style build and run integration
- –Advanced setups require careful tuning of block lengths and decoder settings
- –API integration into streaming systems needs extra glue around batching and timing
Best for: Fits when research teams need configurable FEC encoders and decoders with repeatable BER and latency testing.
Kakadu Software
enterpriseJPEG2000 codec toolkit with error resilience and forward error correction for satellite and medical imaging.
Kakadu SDK processing stages designed for controlled, scriptable FEC parameter sweeps across batch data sets.
Kakadu Software targets forward error correction workflows with an emphasis on applying channel coding and decoding to real media and transport pipelines. It provides the Kakadu SDK and associated command line tools for configuring FEC-related processing stages inside end-to-end data handling.
- +Command-line driven workflow supports repeatable FEC experiments and batch runs
- +Integration into existing media and transport processing pipelines via SDK components
- +Deterministic configuration knobs for coding parameters used in controlled tests
- +Automation-friendly interface supports scripting without extra glue code
- –FEC-centric setups can require nontrivial configuration depth across pipeline stages
- –API surface is more specialized for Kakadu-style workflows than general networking stacks
- –Debugging FEC behavior often depends on understanding codec parameter interactions
- –Less direct fit for environments that only need simple link-layer retransmission
Best for: Fits when media or transport pipelines need deterministic error resilience from configurable coding stages.
Kodo
vertical specialistA network coding software library for reliable data transmission and packet loss recovery.
Steinwurf Kodo’s encoder-to-decoder packet pipeline is designed for direct symbol recovery across lossy packet streams.
Kodo delivers forward error correction using a code generation and encoding workflow built around the Steinwurf Kodo ecosystem. It focuses on end-to-end packet coding for unreliable links where the receiver needs to reconstruct original data after losses.
- +Code and packet workflows map directly to loss recovery needs
- +Encoder and decoder composition supports custom transport and framing
- +Symbol-based operation fits burst and packet-loss recovery patterns
- +Fits systems that already depend on Steinwurf library conventions
- –Correctness depends on matching symbol size and packetization choices
- –Higher effort than simple FEC libraries when adding new transports
- –Performance tuning requires understanding coding parameters and workloads
- –Operational visibility is limited without extra instrumentation around decoding
Best for: Fits when a team needs programmable, symbol-level FEC for custom transports and link-loss recovery workflows.
Conclusion
After evaluating 10 forward error correction software, Codec2 stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Forward Error Correction Software
Forward error correction software applies encoding and decoding logic to add structured redundancy so receivers can recover from errors without retransmission. This buyer’s guide covers Liquid DSP, MATLAB Communications Toolbox, NVIDIA Sionna, Kakadu Software, Codec2, GNU Radio, Rohde & Schwarz VSE, Kodo, AFF3CT, and TurboConcept FEC IP Cores.
The evaluation emphasizes how each tool handles reproducible experiments, whether error-rate measurement stays tied to controlled inputs, and how much automation and API surface supports parameter sweeps. The guide also compares integration depth across FEC workflows, including DSP-first harnesses like Liquid DSP and simulation-first stacks like NVIDIA Sionna and AFF3CT.
Forward Error Correction Software for encoding, decoding, and measurable channel performance
Forward error correction software provides configurable encoder and decoder workflows that transform payload bits into coded symbols and recover original data after channel impairment. Tools such as Liquid DSP focus on bit-accurate encode and decode harnesses where controlled channel inputs directly produce measurable error-rate results.
Some platforms prioritize iterative decoding control and coding-graph experimentation for LDPC workflows, with MATLAB Communications Toolbox offering iterative decoding tooling and per-iteration convergence behavior and NVIDIA Sionna using a graph-structured simulation approach with soft-information message passing. Other options prioritize end-to-end pipeline execution, such as GNU Radio running FEC encode and decode inside streaming schedulers alongside modulation and channel modeling.
FEC software evaluation criteria for reproducible coding and decoder workflows
FEC software is judged on whether encode and decode behavior can be repeated with controlled inputs and controlled parameters. That repeatability matters because BER and PER measurements become defensible only when the harness ties coded bits, channel assumptions, and decoder settings to one run definition.
Bit-accurate encode and decode harnesses tied to controlled inputs
Liquid DSP provides bit-accurate encode and decode harnesses where error-rate measurements follow directly from controlled channel inputs. Codec2 also ships built-in channel coding test workflows that measure BER outcomes for chosen code parameter sets.
Iterative decoding control for LDPC-style convergence experiments
MATLAB Communications Toolbox includes iterative decoding tooling with per-iteration control that supports LDPC workflows and convergence measurement. NVIDIA Sionna uses graph-structured coding simulation with soft information message passing for iterative decoder experiments.
Streaming integration that keeps FEC inside a real execution chain
GNU Radio runs FEC encode and decode blocks inside streaming schedulers so decoding operates alongside real-time synchronization and channel modeling. Kakadu Software instead targets deterministic, scriptable FEC parameter sweeps across batch data sets through SDK processing stages.
Deterministic pipeline composition from encoder to decoder for packet loss recovery
Kodo designs an encoder-to-decoder packet pipeline for direct symbol recovery across lossy packet streams. Kodo correctness depends on matching symbol size and packetization choices, which makes its workflow behavior distinct from generic FEC libraries.
Latency-aware simulation harness that couples channel models to decoders
AFF3CT couples channel models to decoders so runs produce measurable throughput and latency outputs in addition to error-rate results. GNU Radio can support channel modeling and decoding together, but AFF3CT is specifically positioned around direct throughput and latency measurement through its harness.
Verification-style run control tied to coded payload handling and decoder configuration
Rohde & Schwarz VSE uses a verification-style execution workflow that ties coded payload handling and decoder configuration to measurement-oriented run control. This approach targets RF-to-decoder experiments, which differs from research-focused batch harnesses like Kakadu Software.
Choose FEC software by workflow shape: experiment harness, decoder research, or pipeline integration
Different FEC stacks optimize different failure modes, and the fastest way to pick the right one is to match the workflow shape to the integration point. The key fork is whether FEC lives in a bit-accurate experiment harness, inside an iterative decoding research model, or inside a streaming execution chain.
Map FEC usage to the execution point in the system
If FEC verification must start from controlled channel inputs and end with repeatable BER measurements, Liquid DSP fits because its bit-accurate encode and decode harnesses keep channel inputs tied to error-rate outputs. If the priority is embedding FEC into an RF and modulation chain at execution time, GNU Radio fits because its FEC blocks run inside GNU Radio streaming schedulers.
Select an iterative-decoding philosophy based on how decoder behavior is explored
If decoder behavior must be driven by per-iteration controls for LDPC workflows, MATLAB Communications Toolbox fits because it provides iterative decoding tooling with per-iteration convergence behavior. If decoder experiments must be structured around a graph-structured model with soft information message passing, NVIDIA Sionna fits because it is Python-first and batch-automatable for iterative decoding research.
Decide whether the harness needs latency and throughput measurements built in
If the evaluation must include measurable latency and throughput directly from the decoder and channel model coupling, AFF3CT fits because its simulation harness is built for direct throughput and latency measurement alongside BER. If batch dataset processing with deterministic FEC stage configuration is the priority, Kakadu Software fits because its Kakadu SDK processing stages target controlled, scriptable FEC parameter sweeps across batch data sets.
Pick a packet-loss recovery workflow when framing matters
If the workflow centers on symbol recovery across lossy packet streams, Kodo fits because its encoder and decoder composition is built as a packet pipeline. If the goal is reference-grade channel coding test workflows for BER testing where orchestration stays minimal, Codec2 fits because it provides practical channel test harnesses with parameterized code settings.
Use verification-style run control when RF test integration drives configuration
If coded payload handling and decoder configuration must stay attached to measurement-oriented run control in RF-centric testing, Rohde & Schwarz VSE fits because it uses a verification-style execution workflow tied to decoder configuration. If hardware deterministic timing is the primary constraint, TurboConcept FEC IP Cores fits because it targets hardware-first physical-layer decoder integration with deterministic timing.
Who benefits from these FEC software workflows
FEC software buyers usually need either reproducible experiment harnesses, research-grade iterative decoding control, or pipeline integration that runs FEC alongside channel modeling and real-time scheduling. The best fit depends on whether errors must be measured under controlled inputs or tolerated through integrated execution with framing and symbol recovery.
Research teams that must repeat BER runs from controlled channel inputs
Liquid DSP supports bit-accurate encode and decode harnesses that keep error-rate measurement directly traceable to controlled channel inputs. AFF3CT also supports repeatable BER testing but adds throughput and latency measurement in its simulation harness.
Teams running LDPC and iterative decoding experiments with per-iteration visibility
MATLAB Communications Toolbox provides per-iteration control for iterative decoding workflows that support convergence behavior analysis. NVIDIA Sionna adds graph-structured simulation with soft information message passing to drive iterative decoder experiments.
SDR and physical-layer integrators that need FEC inside streaming execution chains
GNU Radio runs FEC encode and decode blocks inside streaming schedulers so the full SDR signal chain can be exercised in one execution path. Rohde & Schwarz VSE also targets RF-to-decoder experiments but uses a verification-style run control model tied to measurement workflows.
Media and transport pipelines that require deterministic, batch parameter sweeps
Kakadu Software provides command-line driven workflows for repeatable FEC experiments and batch runs across SDK components. This batch sweep approach differs from interactive iterative decoding research in NVIDIA Sionna and MATLAB Communications Toolbox.
Link-loss recovery builders that need symbol-level framing control
Kodo is designed around encoder and decoder composition as a packet pipeline for symbol recovery in lossy packet streams. Its correctness depends on matching symbol size and packetization choices, which directly impacts integration work for packet-oriented systems.
Common FEC buyer pitfalls that cause wasted integration time
FEC software failures often come from mismatched workflow expectations, where the buyer selects an interface that produces measurements but does not integrate into the intended execution chain. Other failures come from underestimating the effort needed to tune code lengths, alignment, and decoder settings across pipeline stages.
Choosing a simulation-first stack and then expecting it to be a production-ready physical-layer FEC integration layer
NVIDIA Sionna is primarily a simulation tool built for graph-structured coding experiments with soft information message passing. TurboConcept FEC IP Cores is positioned as hardware-first integration for deterministic timing targets rather than a software simulation harness.
Treating batch parameter sweeps as equivalent to real-time streaming FEC behavior
Kakadu Software targets deterministic, scriptable FEC parameter sweeps across batch data sets through SDK processing stages. GNU Radio ties FEC blocks to streaming schedulers so FEC runs alongside modulation, channel simulation, and real-time synchronization.
Underestimating setup effort for code length tuning and stream alignment in SDR pipelines
GNU Radio FEC setups often require code-length tuning and careful stream alignment to maintain correct scheduling and decoding behavior. Liquid DSP can be easier for bit-accurate validation because its harness keeps controlled inputs tied to error-rate outputs without requiring streaming scheduler alignment.
Selecting a packet-loss workflow without planning for symbol sizing and packetization alignment
Kodo correctness depends on matching symbol size and packetization choices, which means framing decisions can block end-to-end recovery if they drift. Codec2 offers reference-grade channel coding test harnesses, but it still requires integration work to embed FEC into packet-oriented stacks.
How We Selected and Ranked These Tools
We evaluated Liquid DSP, MATLAB Communications Toolbox, NVIDIA Sionna, Kakadu Software, Codec2, GNU Radio, Rohde & Schwarz VSE, Kodo, AFF3CT, and TurboConcept FEC IP Cores by weighting features at 40% and ease and value at 30% each. Liquid DSP separated itself by combining configurable coding parameters for redundancy and decoder behavior tuning with bit-accurate encode and decode harnesses that keep error-rate measurement tied to controlled channel inputs.
The ranking also credited repeatable command-line style validation workflows because those workflows reduce measurement variance across FEC parameter sweeps. When a tool focused on iterative decoding control or simulation graph experiments, the score reflected that benefit while still recognizing the integration boundary compared with Liquid DSP harness repeatability.
Frequently Asked Questions About Forward Error Correction Software
How do Liquid DSP and AFF3CT differ for BER and PER measurement workflows?
Which tool is better for soft-decision iterative decoding experiments across code iterations?
When is GNU Radio the better choice for forward error correction deployment alongside SDR processing?
What breaks if FEC code construction and decoder configuration do not match in Kodo packet flows?
Which tool provides a stronger integration story for Python-based automation and batch sweeps?
How do Kakadu Software and Rohde & Schwarz VSE handle deterministic runs for coded payload processing?
What security and operational controls should be verified for FEC automation in GNURadio pipelines?
How do teams typically migrate from a MATLAB verification flow to a non-MATLAB FEC workflow using MATLAB Communications Toolbox?
Where does TurboConcept FEC IP Cores fall short compared with software-centric research tools like AFF3CT or GNU Radio?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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