GITNUXSOFTWARE REVIEW
Education LearningTop 10 Best Resume Scanner Software of 2026
Compare resume scanner software options and outline tradeoffs for job seekers, with Jobscan included for practical shortlist decisions.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
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Framework: Features 40% · Ease 30% · Value 30%
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Jobscan is the best pick for recruiters who need repeatable job-description match scores across many resumes because its keyword gap report ties missing terms to the exact sections used for scoring, while Affinda is a strong alternative for teams building API workflows since it delivers field-consistent structured parsing for downstream ranking and profiling.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Jobscan
Jobscan’s keyword gap report maps missing terms to the exact job description sections used for scoring.
Best for: Fits when recruiters need repeatable job description match scores for many resumes.
Affinda
Editor pickField-consistent structured outputs designed for downstream ranking and candidate profiling.
Best for: Fits when recruiters need repeatable resume normalization at scale via an API workflow.
Resume Worded
Editor pickResume scoring for job description matching that produces a structured, comparable fit signal across candidates.
Best for: Fits when recruiting teams need repeatable resume scoring and job matching at batch screening scale.
Comparison Table
Jobscan
SMBATS resume optimization tool that compares a resume against a job description and scores keyword match.
Jobscan’s keyword gap report maps missing terms to the exact job description sections used for scoring.
Jobscan turns a job description and resume into a match report that explains which keywords and skill phrases are missing or underweighted. It focuses on resume scoring and keyword extraction driven by structured parsing and comparison against a targeted role profile.
Jobscan also supports scanning multiple resumes for the same job description workflow and can generate normalized outputs suitable for downstream review. The strongest fit is teams that need repeatable job description matching results, not only document parsing.
- +Clear keyword gap reporting tied to the specific job description
- +Resume normalization supports consistent re-scoring across candidates
- +Batch scans enable high-throughput job-specific screening workflows
- +Detailed match explanations reduce manual interpretation time
- –Semantic matching can miss role intent when job descriptions are vague
- –Automation and API-based parsing are limited compared with enterprise ATS vendors
- –Layout-heavy resumes sometimes degrade extraction quality
- –Governance controls like RBAC and audit logging are not a primary focus
job seekers revising resumes
Rewrite weak bullets using section feedback
Clearer, more targeted bullets
applicants worried about ATS
Validate parsing and section detection
Fewer parsing and detection issues
Show 2 more scenarios
applicants preparing submissions
Export application-ready resume files
Shareable, submit-ready file
Jobscan produces normalized outputs that can be edited and exported for common document formats.
job seekers applying to roles
Match resume to a specific posting
More aligned resume version
Resume gaps against a selected job description guide targeted edits to improve posting alignment.
Best for: Fits when recruiters need repeatable job description match scores for many resumes.
Affinda
API-firstDocument AI platform offering a resume parser API that extracts candidate data from resumes.
Field-consistent structured outputs designed for downstream ranking and candidate profiling.
Affinda is built around resume parsing accuracy and structured data extraction, with output fields designed for reuse in candidate-job fit and candidate profiling pipelines. The workflow supports resume ingestion at scale, and extracted entities can feed keyword extraction and semantic matching layers without requiring custom OCR parsing in the common path. A key fit signal is the emphasis on consistent field structure, which reduces friction when normalizing resumes before ATS integration or internal CRM enrichment.
A tradeoff appears in pipeline ownership, because higher-value matching outcomes depend on how extracted fields map to each organization’s skills taxonomy and job requirements. Affinda fits best when recruiters run repeated intake cycles and need repeatable normalization across large resume batches. It also fits teams that already operate an API-first recruitment workflow and want deterministic extracted fields feeding their ranking logic.
- +API-based parsing supports batch resume ingestion for high throughput
- +Normalized extraction reduces field variability across resume sources
- +Structured outputs support candidate profiling and downstream matching
- +Integration workflow fits ATS enrichment and internal ranking systems
- –Best matching depends on configuration of skills taxonomy mapping
- –Requires engineering effort to operationalize extraction outputs in ranking
Recruitment operations teams
Standardize intake across resume batches
Fewer manual cleanups
Talent intelligence teams
Feed semantic matching and fit scoring
More reliable candidate-job match inputs
Show 2 more scenarios
HRIS and ATS integration teams
Populate enrichment fields in ATS
Cleaner ATS records
Ingest resumes through an API parsing workflow and map outputs to ATS fields.
Agency recruiters
Normalize candidates from diverse sources
Faster reviewer readiness
Keep extracted candidate data consistent across vendor and referral resume formats.
Best for: Fits when recruiters need repeatable resume normalization at scale via an API workflow.
Resume Worded
SMBResume grading and ATS optimization platform that provides line-by-line feedback on resume content.
Resume scoring for job description matching that produces a structured, comparable fit signal across candidates.
Resume Worded runs resume scanning and keyword extraction to produce structured signals for recruiter workflows. It emphasizes resume scoring for job description matching and generates ATS-friendly parsing outputs that can be reviewed and used for candidate ranking.
The product also supports bulk resume ingestion for faster throughput during high-volume hiring cycles. Resume Worded pairs parsing with configurable evaluation logic that can be tuned around role-specific requirements.
- +Clear resume scoring that maps candidates to job description expectations
- +Bulk resume ingestion supports faster screening for high-volume pipelines
- +Structured extraction turns resumes into reviewable fields for downstream ranking
- +Role-focused keyword extraction improves signal consistency across batches
- –Parsing results can degrade on resumes with nonstandard formatting
- –Automation requires careful configuration to avoid misleading score inflation
- –Advanced matching logic can be harder to tune for niche taxonomies
- –Less suitable for teams needing fully custom parsing beyond the UI
applicants iterating resume edits
Compare revisions against one role
Feedback-led refinement loop
job seekers targeting openings
Prioritize edits for posting relevance
Clearer posting alignment
Show 2 more scenarios
applicants checking ATS readability
Inspect formatting for scanner compatibility
More parser-friendly resume
Resume structure and formatting are reviewed for common scanability and parsing issues.
candidates with vague experience
Strengthen achievement bullet specificity
Stronger action and impact
Feedback helps rewrite experience bullets into action-oriented, evidence-based statements.
Best for: Fits when recruiting teams need repeatable resume scoring and job matching at batch screening scale.
DaXtra
enterpriseResume parsing and candidate data extraction software for recruitment agencies and HR departments.
API-based parsing for batch and event-driven resume ingestion with structured outputs for downstream automation.
Recruiters and HR operations teams use DaXtra to turn resume ingestion into consistent data fields that can feed candidate profiling and ranking steps. The system’s practical value shows up when resumes arrive in mixed formats, including scanned documents where OCR quality affects downstream parsing accuracy. DaXtra fits teams that need structured data extraction at throughput levels beyond manual review.
- +API-driven resume ingestion supports bulk processing workflows
- +OCR-backed parsing helps extract data from scanned resumes
- +Structured output simplifies downstream normalization and enrichment
- +Field extraction supports job requirement mapping steps
- –Configuration effort is needed to keep outputs aligned across formats
- –Semantic matching quality varies when resumes use highly nonstandard layouts
- –Admin governance controls are less visible than in ATS-native tools
- –Integration work is required to connect extracted fields to ranking logic
HR operations teams
Weekly bulk resume ingestion and routing
Faster screening pipeline setup
Recruitment engineering teams
ATS integration via parsing API
Less manual data cleanup
Show 2 more scenarios
Sourcers at staffing firms
Scanned resumes keyword and entity extraction
More eligible candidates surfaced
OCR-backed parsing extracts experience and skills from scans to support candidate-job fit scoring inputs.
Talent managers
Job description driven requirement mapping
More consistent shortlists
Extracted resume fields support job requirement matching beyond basic keyword search signals.
Best for: Fits when recruiters need API-based bulk parsing to standardize candidate data before ATS routing.
VMock
enterpriseAI-based resume scoring platform used by universities and career services to evaluate resume quality.
Resume scoring paired with job description matching to generate candidate-job fit signals for recruiter review.
VMock turns resumes into structured fields for downstream screening workflows, with resume scoring and job description matching designed for recruiter review. It focuses on parsing, normalization, and candidate-job fit outputs that can be consumed by search, ranking, and review steps.
- +Produces structured, normalized resume fields for screening workflows
- +Includes resume scoring and job description matching signals
- +Supports automation pathways for resume ingestion and ranking
- +Provides administration controls for managing screening configuration
- –Parsing quality can vary across uncommon formatting and scanned resumes
- –Automation and governance require deliberate configuration discipline
recruiters screening batches
Score and compare resumes at scale
Faster, more consistent intake
job seekers running resume audits
Track improvements across successive versions
Repeatable improvement tracking
Show 2 more scenarios
students and career coaches
Get section recommendations and rewrite prompts
More readable sections
Users receive section-level recommendations and prompts to fix unclear or weak resume content.
graduates tailoring applications
Revise content toward job requirements
Role-aligned priority areas
A resume is compared against a target description so users revise toward the role's requirements.
Best for: Fits when HR teams need consistent resume scoring signals plus structured extraction for high-volume screening.
RChilli
enterpriseResume parsing and matching API that processes resumes in multiple languages for ATS integration.
Normalization that converts extracted resume fields into consistent records for downstream job-fit and candidate profile use.
RChilli is built around extraction and normalization steps that convert resumes into structured records suitable for ATS integration and candidate ranking pipelines. It supports bulk resume ingestion patterns where throughput and consistent field segmentation reduce manual review load. For teams that rely on job description matching, the normalized output supports repeatable keyword extraction and semantic mapping across candidates. The main evaluation signal is whether extracted entities remain stable across varied layouts and languages in the same parsing run.
- +Structured extraction supports ATS workflows without manual field normalization
- +Batch parsing output consistency reduces variance across recruiters
- +Entity segmentation helps build candidate profiles from noisy resumes
- +OCR handling supports mixed digital and scanned documents
- –OCR accuracy drops on low-resolution scans and tight crops
- –Field mappings need careful configuration per downstream schema
- –Complex matching logic often requires additional rules outside parsing
- –Some layout-heavy PDFs need retries to reach usable extraction quality
Recruiting ops teams
Bulk ingest resumes for a shared pipeline
Faster queue triage
HR analytics teams
Candidate profiling for skills and history
Cleaner analytics datasets
Show 2 more scenarios
Talent acquisition recruiters
Job description matching with stable fields
More consistent rankings
Normalized fields improve repeatable job-fit calculations and keyword alignment across candidates.
Support teams for hiring
Resolve parsing errors at scale
Reduced manual rework
Structured outputs make it easier to flag low-confidence extractions for targeted review.
Best for: Fits when HR teams need batch resume parsing with structured fields for ranking and profile building.
HireAbility
enterpriseCloud-based resume and CV parsing service that extracts structured candidate data for recruitment workflows.
Batch resume ingestion with configurable parsing outputs designed for high-volume recruiter imports.
HireAbility scans resumes and turns them into structured candidate records for recruiters and HR teams. The product focuses on parsing, normalization, and keyword extraction that feed downstream ATS integration workflows.
- +Batch resume ingestion supports higher-throughput screening workflows
- +Structured extraction reduces manual reformatting before ATS import
- +Keyword extraction outputs are consistent across typical resume layouts
- +Workflow configuration fits recruiter review and handoff steps
- –Semantic matching depth can lag tools that specialize in job fit scoring
- –Resume anonymization controls are not as granular as governance-first vendors
- –OCR accuracy can degrade on heavily stylized or low-resolution scans
- –API-based parsing requires more integration setup than GUI-first tools
staffing and corporate recruiters
Batch-process PDF and DOCX resumes
Less manual data entry
recruiters and talent acquisition
Rank resumes against role requirements
Repeatable candidate shortlist
Show 2 more scenarios
HR technology vendors
Add structured resume data workflows
Structured inputs for ATS
Parsing and matching functions generate structured recruiting data that can be used in connected platforms.
hospitality recruiting teams
Organize recurring recruiting handoffs
More organized candidate flow
Structured intake outputs support sourcing, screening, and interview coordination steps for recurring roles.
Best for: Fits when recruiting teams need consistent structured extraction and batch parsing into existing screening workflows.
RezScore
SMBAutomated resume grading tool that analyzes resume content and provides a scored evaluation.
Resume scoring built around job-fit signals for candidate ranking in high-volume review queues.
RezScore focuses on resume scoring and parsing so recruiters and HR teams can turn unstructured resumes into structured candidate data. It supports resume ingestion workflows with extraction and normalization steps geared toward keyword-based job description matching and candidate ranking.
- +Resume scoring outputs designed for consistent candidate-job fit comparisons
- +Structured extraction supports downstream keyword matching and ranking workflows
- +Bulk resume ingestion supports higher-throughput review pipelines
- +Normalization reduces variance from differing resume layouts
- –Semantic matching quality can vary on unconventional resume formats
- –Scoring configuration needs careful job taxonomy alignment
- –Limited evidence of fine-grained controls for deduplication edge cases
- –API-based parsing coverage may require engineering work for edge OCR cases
recruiters and HR teams
Convert unstructured resumes into fields
Consistent candidate fields
HR teams running bulk screening
Ingest resumes for repeatable evaluation
Repeatable candidate ranking
Best for: Fits when teams need repeatable resume scoring and structured outputs for ATS-adjacent evaluation workflows.
Enhancv
SMBResume builder platform that includes an ATS resume scanner to check compatibility with applicant tracking systems.
Guided resume-driven field normalization that keeps parsed profiles recruiter-readable across many submissions.
Enhancv turns resume content into structured candidate details using its guided resume builder data flow and resume parsing views. It focuses on readable extraction that supports downstream review and candidate profiling rather than only keyword lists.
- +Structured section extraction makes recruiter review faster than raw text
- +Consistent normalization of experience and skills reduces manual cleanup
- +Candidate profiles remain readable after parsing, not just machine output
- +Upload-to-review workflow supports batch screening without heavy tooling
- –Limited visibility into parsing behavior for edge cases like unusual layouts
- –Deep ATS integration and API options can be constrained for automation-heavy stacks
- –Semantic matching tuning options are less granular than tools built for ranking
- –Resume deduplication and identity resolution workflows are not the primary focus
job seekers rebuilding resumes
Draft a structured resume in sections
Structured resume draft
applicants reviewing near-final drafts
Check readiness using structured feedback
More application-ready resume
Show 2 more scenarios
applicants applying to roles
Adjust ordering and keywords for a posting
Better-aligned application
Users change section order, wording, and keywords around a target job description for better fit.
frequent applicants and career changers
Maintain multiple role-focused variants
Role-focused resume variants
The resume narrative and priorities are iterated across different roles, industries, and directions.
Best for: Fits when recruiting teams want readable parsed profiles for high-volume screening without deep workflow engineering.
Textkernel
enterpriseEnterprise resume parsing and matching software that extracts structured data from resumes for recruitment systems.
Configurable resume normalization and enrichment feeding candidate ranking logic through an API-first workflow.
Textkernel focuses on document-to-structured-data extraction for recruiting workflows, with a resume parsing pipeline designed for downstream candidate matching and ranking. It supports API-based parsing and configurable ingestion for high-volume resume intake, including handling of varied formatting and document layouts.
- +API-based parsing supports controlled resume ingestion at scale
- +Structured extraction improves downstream candidate-job fit scoring workflows
- +Normalization reduces friction for ATS integration and internal ranking logic
- +Bulk resume processing supports high-throughput recruiting pipelines
- –Configuration depth can slow initial rollout for teams without technical support
- –Workflow governance requires deliberate setup across parsing and enrichment steps
recruiting operations teams
Ingest large resume volumes automatically
Less manual data entry
international HR and staffing teams
Normalize information across languages
Broader consistent candidate coverage
Show 2 more scenarios
HR technology and integration teams
Feed parsed data into ATS workflows
Structured recruiting data in systems
Organizations send parsed resumes and matching signals into applicant tracking and recruiting workflows.
HR software developers
Embed parsing and matching in products
Structured talent features in-app
Parsing, extraction, and matching capabilities are added into recruiting products to enrich talent intelligence.
Best for: Fits when recruiters need API-driven parsing, structured normalization, and dependable bulk ingestion for consistent matching.
Conclusion
After evaluating 10 resume scanner software, Jobscan 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 Resume Scanner Software
Resume scanner software converts uploaded CVs and scanned resumes into structured fields that recruiters and HR teams can rank, route, and compare. This guide covers Affinda, DaXtra, Textkernel, and the other top tools built for parsing accuracy, scoring repeatability, and integration depth.
The strongest options in this shortlist focus on controlled resume normalization plus an automation-ready output workflow that fits bulk resume ingestion. Hiring teams evaluating resume scanner software across high-volume pipelines should compare how each vendor handles OCR-backed extraction, skills mapping consistency, and API-based ingestion.
Resume Scanner Software for structured resume extraction, scoring, and ATS-ready workflows
Resume scanner software performs resume parsing and resume normalization so the same candidate data ends up in consistent structured fields across different resume formats. Tools such as Affinda and DaXtra emphasize API-based parsing outputs designed for downstream automation like candidate profiling and ATS routing.
Beyond extraction, many solutions add job description matching signals so recruiting teams can apply candidate-job fit scoring in repeatable screening queues. Jobscan and Resume Worded focus more on keyword gap reporting and structured scoring signals mapped to job description sections, while others prioritize normalization consistency for ranking inputs.
What to validate in Resume Scanner Software extraction, normalization, and matching
Resume scanner software only becomes actionable when it outputs structured fields that downstream ranking and routing logic can consume without manual cleanup. Affinda and DaXtra focus on API-based parsing outputs that support bulk resume ingestion workflows and consistent downstream automation.
API-based resume parsing for bulk ingestion and automation
Affinda and DaXtra support API-based parsing designed for batch resume ingestion, so recruiters can standardize candidate data before ATS routing and profiling. Textkernel also emphasizes an API-first workflow for controlled resume ingestion at scale.
Field-consistent structured outputs for downstream ranking and profiling
Affinda produces field-consistent structured outputs that reduce variability across resume sources and support candidate profiling. RChilli converts extracted fields into consistent records so job-fit and candidate profile workflows see stable inputs.
OCR-backed parsing for scanned resumes and non-text submissions
DaXtra pairs API-based parsing with OCR-backed extraction to pull data from scanned resumes. RChilli’s parsing output remains dependent on OCR quality for low-resolution scans and tight crops.
Job description matching signals with interpretable scoring outputs
Jobscan provides keyword gap reporting mapped to exact job description sections used for scoring. Resume Worded and VMock generate resume scoring signals designed for repeatable candidate-job fit comparisons in high-volume queues.
Normalization configuration for skills taxonomy mapping and schema alignment
Affinda’s matching quality depends on skills taxonomy mapping configuration for consistent outputs across recruiters. RChilli and Textkernel require field mapping and workflow governance configuration to align parsed outputs with downstream schemas.
Choose based on workflow shape: normalization first versus scoring first versus OCR-first ingestion
Teams that need standardized candidate records for routing and profiling should prioritize field-consistent normalization and API-based parsing outputs. Affinda, RChilli, and Textkernel are built around structured extraction that reduces variance across resume formats and supports repeatable screening inputs.
Map the target workflow to an output contract
If the downstream system needs consistent, structured fields for candidate profiling and routing, prioritize Affinda, RChilli, or Textkernel because their structured extraction targets stable records for downstream ranking logic. If the downstream system needs scoring signals tied to the job description, prioritize Jobscan or Resume Worded because their outputs center on job match evaluation rather than only normalization.
Decide whether the ingestion model is batch, event-driven, or bulk routing
If resume ingestion must run as batch or event-driven automation through an API, prioritize DaXtra because its API-driven bulk processing supports structured outputs for downstream automation. If the team runs high-volume screening imports where output consistency across files matters most, RChilli focuses on batch parsing output consistency to reduce variance across recruiters.
Validate scanned-resume coverage against the OCR failure modes the team sees
If the candidate pool includes scanned resumes, confirm DaXtra’s OCR-backed parsing extracts key fields from scanned inputs as part of the workflow test set. If the team regularly receives low-resolution scans and tight crops, test RChilli for OCR accuracy dips before committing to it as the normalization layer.
Choose the scoring layer only after checking job description interpretability
If recruiters need to see exactly which job description sections drive missing-term results, Jobscan’s keyword gap report maps missing terms to job description sections used for scoring. If the job descriptions are vague and recruiters still need a stable signal, validate semantic matching performance because Jobscan’s semantic matching can miss role intent under vagueness.
Confirm skills taxonomy mapping and field mapping governance capacity
If the team can invest engineering effort in operationalizing taxonomy mapping, Affinda’s best results depend on configured skills taxonomy mapping for matching stability. If the team lacks technical support, Textkernel and RChilli can still work but initial rollout slows when field mappings must be carefully configured per downstream schema.
Who Resume Scanner Software buyers should be targeting
Recruiters and HR teams need resume scanner software when resume ingestion and screening require consistent structured outputs across many resume formats. The best fit depends on whether the team’s core workflow is normalization for routing or scoring for job match evaluation.
Recruiting teams building API-driven candidate ingestion into ATS routing
DaXtra and Affinda are designed for API-based parsing workflows that support bulk resume ingestion and structured outputs for downstream automation like ATS routing and candidate profiling.
HR teams running high-volume screening queues that rely on stable normalization inputs
RChilli focuses on normalization consistency that reduces field variability across resume sources, which supports batch resume parsing into existing screening workflows.
Recruiters who need explainable job match gaps tied to specific job description sections
Jobscan outputs keyword gap reporting mapped to exact job description sections used for scoring so recruiters can interpret why a resume scores lower or higher.
Teams that want structured recruiter-readable profiles without deep workflow engineering
Enhancv emphasizes structured section extraction that keeps parsed profiles recruiter-readable across many submissions, which reduces manual cleanup during review.
Common failure modes when selecting Resume Scanner Software
Selection mistakes usually come from assuming parsing quality will translate directly into reliable ranking or routing. OCR sensitivity and configuration dependencies can create field drift or unstable matching signals across resume formats.
Selecting a scoring-first tool without testing parsing stability on the team’s real resume formats
Resume Worded and VMock can produce structured scoring signals, but parsing results degrade on resumes with nonstandard formatting, so a pilot should include those formats before rolling out scoring-only workflows.
Assuming OCR-backed extraction will behave the same across scan quality and crop tightness
DaXtra supports OCR-backed parsing for scanned resumes, while RChilli’s OCR accuracy drops on low-resolution scans and tight crops, so the test set must mirror the scan conditions the team receives.
Underestimating configuration and governance work required for taxonomy mapping and schema alignment
Affinda’s best matching depends on skills taxonomy mapping configuration, and Textkernel requires workflow governance setup across parsing and enrichment steps, so teams without operational capacity should validate implementation timelines.
Over-trusting semantic matching when job descriptions are vague
Jobscan’s semantic matching can miss role intent when job descriptions are vague, so teams should validate whether keyword gap reporting and scoring align with recruiter expectations for ambiguous postings.
How We Selected and Ranked These Tools
We evaluated Affinda, DaXtra, Textkernel, and the rest of this shortlist using feature coverage and integration depth for resume scanner software workflows. Features accounted for 40% of the score and focused on structured extraction outputs, normalization consistency, and job description match signals where present.
Ease and value each accounted for 30% of the score and reflected how quickly teams could operationalize batch resume ingestion outputs into downstream screening logic. Affinda led the ranking because field-consistent structured outputs support repeatable resume normalization at scale via an API workflow and because batch ingestion reduces field variability across resume sources.
Frequently Asked Questions About Resume Scanner Software
How do Affinda and Textkernel differ in API-based resume ingestion outputs?
Which tool is better for OCR-heavy parsing when resumes arrive as scanned PDFs?
How does DaXtra handle bulk resume ingestion for recruiter workflow automation?
What breaks if job-fit logic depends only on keyword matching instead of structured fields?
How does VMock produce candidate-job fit outputs for recruiter review queues?
Which tool provides keyword-gap explanations tied directly to job description sections?
How do HireAbility and RezScore differ when the goal is predictable ATS-adjacent evaluation fields?
What data migration work is usually required when switching from one resume parser to another?
How do admin controls and governance surface differently across VMock and HireAbility?
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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