Study Types & Reporting Checklists
Once the study type is established, it selects the reporting checklist the review runs against. This section documents the supported study types, their groupings, and how checklists are structured.
Supported study types
The module supports a large catalogue of study types, organised into groups:
Experimental Studies Randomized Controlled Trial (RCT), Quasi-Experimental Study, Pragmatic Trial, Adaptive Trial, N-of-1 Trial, Non-Inferiority Trial, Cluster Randomized Trial, Animal Experiment, Stepped Wedge Trial
Observational Studies Cohort Study, Case-Control Study, Cross-Sectional Study, Retrospective Cohort Study, Case Report, Case Series, Diagnostic Accuracy Study, Real World Evidence Study, Ecological Study, Registry-Based Study, Observational Comparative Effectiveness Research (CER), Big Data / Secondary Data Analysis (RECORD-BD)
Synthesis & Review Systematic Review & Meta-Analysis, Health Technology Assessment, Economic Evaluation Study
Medical Device & Quality Qualitative Study, Post-Market Clinical Follow-Up (PMCF), Modeling and Simulation Studies, Real-Time Monitoring / Wearable Device Study, Post-Market Surveillance (PMS) Study
Specialized Studies Mixed Methods Study, Theoretical/Modeling Study, Implementation Study, In-Vitro / Translational Research, Genomic / Genetic Research, Simulation-Based Clinical Education Study, AI / Machine Learning Study, Digital Therapeutics / Mobile Health Intervention Study, Precision Medicine / Omics Integration Study
Safety & Pharmacovigilance Pharmacovigilance / Adverse Event Reporting Study
Quality & Reporting Standards Good Biostatistical Reporting
Reporting guidelines behind the checklists
Each study type's checklist is built around the internationally recognised reporting guideline for that design:
| Guideline | Design it governs |
|---|---|
| CONSORT (and extensions) | Randomized controlled trials |
| STROBE (and extensions) | Observational studies (cohort, case-control, cross-sectional) |
| PRISMA | Systematic reviews and meta-analyses |
| ARRIVE | Animal experiments |
| CARE | Case reports |
| STARD | Diagnostic accuracy studies |
| CHEERS | Economic evaluations |
| SPIRIT-AI / CONSORT-AI / CLAIM / TRIPOD-AI | AI / machine-learning studies |
| RECORD / RECORD-BD | Routinely-collected and big-data analyses |
Checklist structure
Each checklist is a set of questions with two key properties:
| Property | Meaning |
|---|---|
| question | The reporting item to check |
| category | Either major or minor -- the severity if not adequately addressed |
| sort_order | Display order in the checklist |
The major/minor distinction is central:
- Major items -- critical reporting elements; failing them signals a substantive completeness problem
- Minor items -- important but less critical; failing them indicates a smaller gap
This classification feeds directly into both manual and AI review output.
Where checklist content lives
Checklist questions are stored in the platform's data service (a checklist_questions table), keyed to each study type. When a review begins:
- Takes the determined study type
- Fetches that type's checklist questions, ordered by sort_order
- Presents them for review (manually or to the AI)
Questions live in the data service rather than application code, so checklists can be updated centrally -- guideline revisions and new study types can be added without changing the interface.
From study type to review
Study type determined (survey / manual / AI)
|
Checklist for that type fetched from data service
|
Items (each major or minor, in sort order) presented
|
Review: Manual or AI
|
Compliance report, with findings split into major / minor issues
The study type is the hinge of the whole module: it determines which guideline's items your manuscript is checked against, and therefore what "compliant" means for your specific design.