Diagnostic meta-analysis

Diagnostic evidence, structured from the 2×2 table up.

Keep true positives, false positives, false negatives and true negatives explicit, then move toward pooled diagnostic accuracy outputs without losing the test/reference-standard context.

Create diagnostic projectSee QUADAS-2
✓ TP / FP / FN / TN✓ Sensitivity & specificity✓ SROC-oriented outputs
Diagnostic 2×2 evidenceComplete
Condition +
Condition −
Test +
0
0
Test −
0
0
90.3%Sensitivity
85.0%Specificity
Diagnostic workflow

Preserve what the numbers mean.

Diagnostic evidence depends on the relationship between the index test, reference standard, threshold and target condition—not just four numbers in a table.

2×2

Structured diagnostic cells

Store TP, FP, FN and TN in a diagnostic-specific dataset rather than flattening them into generic outcomes.

Se

Accuracy measures

Review sensitivity, specificity and related diagnostic metrics from reconciled evidence.

S

SROC visualization

Inspect the study-level diagnostic evidence and summary displays where supported by the analysis workflow.

Quality assessment

Diagnostic accuracy needs diagnostic bias assessment.

QUADAS-2 fits the diagnostic project structure so patient selection, index test, reference standard, and flow/timing remain visible beside the evidence.

1
Patient selectionConsider spectrum, sampling and inappropriate exclusions.
2
Index testAssess interpretation, thresholds and prespecification.
3
Reference standardConsider whether the reference standard correctly classifies the condition.
4
Flow and timingReview interval, verification and inclusion in analysis.
Diagnostic dataset checkReady
Consistency check: diseased total = TP + FN and non-diseased total = FP + TN.
93condition positive
107condition negative

Bring your review into one connected workspace.

Start with citation screening and keep evidence, reviewer decisions, datasets and analysis connected from beginning to end.

Request a demo