Docs · Research
The systematic-review workspace
Protocol registration, search strategies with MeSH and PRISMA-S, dual screening with calibration, full text, conflicts, references, risk of bias, GRADE, PRISMA 2020 diagram, grey literature and audit — one sub-app per project.
The stages
Project → Review opens an overview with one card per stage and the live numbers. The navigation follows PRISMA order:
| Page | What you do there |
|---|---|
| Protocol | Fill the PROSPERO / PRISMA-P fields, set eligibility criteria (PICO, inclusion, exclusion, one or two reviewers), record the registration id, lock the protocol; edits after locking are dated amendments; export as Markdown. |
| Search | Build one strategy per database from concept groups (title/abstract, controlled vocabulary or all fields), look up MeSH descriptors, keep the generated or hand-edited syntax, record platform, vendor, limits, filters and every run; export the PRISMA-S appendix. Records are imported on the Sources page. |
| Screening | Title/abstract and full-text queues with keyword highlights, notes, keyboard shortcuts (I / E / M, N / P, U undo), blinded co-reviewer votes, AI hints revealed on demand where an agent or the platform has recorded them, and pilot calibration (Cohen's κ between the two most active reviewers). |
| Full text | Records that passed title/abstract: retrieve open-access full texts, read them, decide with reasons. |
| Conflicts | Disagreements with both votes; an editor records the resolution. |
| Records | Every imported record with its stage, each reviewer's decision, reasons and notes; filter by status, search. |
| Extraction | Evidence tables with anchored quotations (the Evidence page). |
| Risk of bias | RoB 2 or ROBINS-I per included study; suggestions from an AI agent are labelled and stay suggestions until you confirm them. |
| GRADE | Assemble counts per outcome, run the meta-analysis, grade certainty, insert Summary of Findings and risk-of-bias tables into a manuscript. |
| PRISMA | The PRISMA 2020 flow diagram from live counts plus the numbers only you know (registers, other sources, reports not retrieved); export SVG or 300-dpi PNG. |
| Grey literature | Checklist of registers, preprints, theses, conference abstracts, regulatory documents and expert contact with dates and addresses. |
| Audit | Every recorded action on the project. |
With your own AI agent
Connect an agent from the project's Your AI tab and ask it to work the review with you: it can set the protocol, run the search strategy, add and remove records, record screening decisions with reasons, save risk-of-bias and GRADE judgements, assemble and pool the meta-analysis and insert the review tables into a manuscript. Everything it records is labelled and stays advisory: a screening call is a vote until a reviewer confirms it, and a risk-of-bias or GRADE row is a suggestion until someone saves it.
The buttons that ask the platform's own models to do this — “Ask the assistant” on the screening desk, “Propose with the assistant” on risk of bias and GRADE, Pre-fill and Extract on evidence, and the Workflows tab with its systematic-review preset — appear only where an administrator has switched platform AI on. Your connected agent writes the same rows through the same tools either way.
Working without any AI
Every stage runs by hand. Search and import references yourself under Sources and References; screen with the Include / Exclude / Maybe bar (dual screening, conflicts); record risk of bias and GRADE judgements on their pages.
- Extraction by hand: Project → Evidence → “Extract by hand”. Pick the study, fill the fields the paper reports (design, population, N, intervention, comparator, primary outcome, key finding, effect measure with estimate and 95% CI, limitations, funding, registration), optionally with page numbers and supporting quotes. The row appears in the evidence table marked “manual” and verified by you.
- Pooling: Project → Analysis → Meta-analysis → “Import studies…” pulls effect estimates from the extraction table (generic estimate + CI), or reads one study per row from any dataset with column mapping — event counts, means and SDs, or estimates. Type or paste studies directly too.
- Reporting: PRISMA numbers come from your screening decisions; the reporting checklist and the manuscript work the same either way.
AI is optional at every step: it proposes, you decide. Entries made by hand and rows an agent extracted sit in the same table, each marked with its origin, so the audit trail stays complete.
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