Quality Assessment
Risk of bias assessment with four validated tools (RoB 2.0, ROBINS-I, NOS, QUADAS-2) and GRADE evidence certainty rating — AI-assisted, human-confirmed.
Quality Assessment
Study quality isn't a checkbox. It's the foundation of credible evidence synthesis.
Including low-quality studies without appropriate assessment undermines the entire purpose of a systematic review. Peer reviewers scrutinize this step more than almost any other. mapped integrates four validated risk of bias tools and the GRADE framework into a single, consistent workflow — with AI assistance that ensures reliability across your entire review.
Risk of Bias Assessment
mapped supports the four major risk of bias instruments, each designed for a specific study type:
RoB 2.0 — Randomized Controlled Trials
The Cochrane Risk of Bias tool 2.0 assesses five domains:
- Randomization process — was allocation truly random and concealed?
- Deviations from intended interventions — were there protocol deviations?
- Missing outcome data — was attrition balanced and handled appropriately?
- Outcome measurement — were assessors blinded?
- Selection of reported result — is there evidence of selective reporting?
Each domain receives a judgment: Low risk, Some concerns, or High risk. mapped's Advanced Reasoning Engine generates preliminary assessments with supporting rationale, which you review and confirm per domain.
ROBINS-I — Non-Randomized Studies of Interventions
For cohort studies, case-control studies, and other non-randomized designs:
- Confounding — were important confounders controlled?
- Selection of participants — was selection related to intervention and outcome?
- Classification of interventions — was intervention status well-defined?
- Deviations from intended interventions — similar to RoB 2.0 but for non-randomized contexts
- Missing data — as above
- Measurement of outcomes — as above
- Selection of reported result — as above
Newcastle-Ottawa Scale (NOS) — Observational Studies
A star-based system (maximum 9 stars) assessing:
- Selection (4 stars) — representativeness, selection of controls, ascertainment of exposure
- Comparability (2 stars) — controlling for confounders
- Outcome (3 stars) — outcome assessment, follow-up duration and adequacy
QUADAS-2 — Diagnostic Accuracy Studies
For studies evaluating diagnostic tests:
- Patient selection — appropriate spectrum? Consecutive enrollment?
- Index test — was the threshold pre-specified?
- Reference standard — is it likely to correctly classify the condition?
- Flow and timing — did all patients receive the reference standard? Appropriate interval?
AI-Assisted Assessment
mapped's Advanced Reasoning Engine assists with risk of bias assessments by:
- Reading the full text of each included study
- Generating domain-level judgments with supporting quotes from the paper
- Identifying potential concerns that human reviewers might overlook (e.g., selective reporting patterns)
- Maintaining consistency across studies — applying the same standards to study #1 and study #50
Every AI-generated judgment is presented alongside the source text. You accept, modify, or override each one. The dual-reviewer protocol applies here too — two independent assessments, followed by conflict resolution.
GRADE Evidence Certainty
After assessing individual study quality, the GRADE framework rates the overall certainty of evidence for each outcome:
Downgrade Factors
| Factor | Question |
|---|---|
| Risk of bias | Are the included studies at high risk of bias? |
| Inconsistency | Do results vary substantially across studies? |
| Indirectness | Does the evidence directly address your question? |
| Imprecision | Are confidence intervals wide? Small sample size? |
| Publication bias | Is there evidence that studies are missing? |
Upgrade Factors
| Factor | Question |
|---|---|
| Large effect | Is the effect size so large that confounding alone can't explain it? |
| Dose-response | Is there a clear dose-response gradient? |
| Residual confounding | Would residual confounders reduce rather than increase the effect? |
mapped walks you through each factor for each outcome, producing a final rating: High, Moderate, Low, or Very Low.
Summary of Findings Tables
mapped generates Summary of Findings (SoF) tables that combine:
- Effect estimates from your meta-analysis
- GRADE certainty ratings per outcome
- Absolute risk differences (when applicable)
- Footnotes explaining downgrade/upgrade decisions
These tables follow Cochrane formatting and are ready for manuscript insertion.
Visualizations
Risk of bias results are automatically visualized:
- Traffic light plots — domain-level judgments per study (green / yellow / red)
- Summary bar charts — proportion of studies at each risk level per domain
- Risk of bias summary tables — tabular format for manuscripts
All visualizations are exportable to PNG, SVG, or directly inserted into your manuscript draft.
Why This Step Matters
Reviewers and editors increasingly reject systematic reviews with superficial quality assessments. "All studies were assessed using RoB 2.0" isn't enough — you need domain-level justifications, inter-reviewer agreement documentation, and transparent GRADE judgments. mapped provides all of this in a structured, auditable workflow.
Next step: With quality assessed, move to Meta-Analysis →
Data Extraction
Transform research PDFs into structured, editable spreadsheets with AI-powered extraction, Google Sheets integration, and human validation.
Meta-Analysis
R-based statistical engine for systematic reviews — forest plots, funnel plots, publication bias tests, subgroup analysis, and meta-regression, all without writing code.