How Axelium works

Search, import, and study linking

Add records manually, import RIS/BibTeX libraries, generate a search strategy from your protocol, or use citation chasing to find related papers. Axelium deduplicates records and links trial registrations, publications, and companion papers where possible.

• Search results enter the review as candidates, usually pending screening.
• Optional source coverage can broaden recall beyond the primary clinical indexes.
• Known-relevant papers can improve recall as pearls or citation-chasing seeds.
• Source coverage is retained for transparent PRISMA reporting.

A search strategy has two layers: a plan — the population, exposure or intervention, comparator, outcomes, publication date range (whole years, or exact dates when you are reproducing a search run on a specific day), extra terms and exclusions — and the queries built from it for each source. Four actions on the Search page work on these differently, and two of them replace the plan:

• Refine queries — rebuilds the queries from your existing plan. Your edits to the plan are kept. This is the one to use while iterating.
• Rebuild plan + queries from scratch — drafts a completely new plan from your research question, then new queries. Anything you set by hand is replaced.
• Reseed plan heuristically — the same replacement, without the drafting assistant.
• Edit structured plan — opens the plan as editable fields. Saving clears the generated queries, so rebuild them afterwards.

The two replacing actions ask you to confirm, and are disabled once the strategy is locked — so locking a strategy you have tuned by hand is the surest way to keep it.

If the query builder runs out of time mid-build — during a temporary slowdown, for example — it keeps the work it has already done and finishes from there rather than starting over. When it has to fall back to a simpler automatic strategy instead, the Search page says so prominently, and a fallback never overwrites queries the full builder previously saved: rebuild once things recover and nothing is lost.

www.axelium.ai · Search

Screening and eligibility

Screening applies the protocol to every candidate record. AI suggestions can provide a starting point, but reviewer decisions, exclusion reasons, dual-review modes, and conflict adjudication are the source of truth for formal reviews.

• Included records continue to full text and extraction.
• Excluded records keep their reason for PRISMA reporting.
• Unsure records need reviewer attention or adjudication.

Two kinds of record are set aside whatever the protocol says, because neither can contribute participants of its own: study protocols and methods-only papers, which describe a study without reporting its results; and secondary literature — reviews, meta-analyses, editorials, guidelines and economic evaluations — which re-reports studies already eligible in their own right. Including the latter would count the same trial more than once. Subgroup and follow-up reports of a named trial are unaffected.

For comparison-based questions, screening also records whether each study analyses the comparison the review asks about — reporting outcomes separately for the compared groups, or estimating an effect of the contrast — or merely mentions it, for example as a baseline characteristic or a subgroup label. Reviews whose comparison vocabulary is common in off-topic abstracts can opt to require the analysed comparison for inclusion: mention-only studies are then excluded, and studies where the text does not reveal which are held for reviewer attention.

Full text, supplements, and manual upload

Full-text retrieval collects PDFs, supplements, and other source documents needed for extraction or qualitative coding. Open-access retrieval is attempted first; when a document remains unavailable, users can upload it manually or capture it with the browser companion if they have lawful access.

• Supplements can include appendices, spreadsheets, and Word documents.
• Parsed documents become available for source-backed extraction.
• Blocked or missing documents stay visible so reviewers can resolve them.

Extraction, coding, and reviewer checks

Quantitative reviews extract structured values such as events, totals, means, standard deviations, time-to-event estimates, or prevalence data. Qualitative reviews code passages, appraise studies, and develop themes. In both cases, outputs should stay connected to source evidence and reviewer decisions.

• Low-confidence or conflicting values should be reviewed before synthesis.
• Qualitative themes and CERQual ratings are proposals until a reviewer finalises them.
• Risk-of-bias and certainty judgements remain reviewer-owned.

www.axelium.ai · Extraction

Synthesis and statistical analysis

The Analysis tab supports pairwise meta-analysis, common sensitivity checks, publication-bias views, qualitative synthesis outputs, and controlled-rollout advanced workflows such as network meta-analysis and ecology/evolution synthesis. The app surfaces plots, tables, and diagnostics; reviewers interpret them against the protocol and the quality of the underlying evidence.

When a method falls outside the curated toolset, you can also run your own — bespoke models, uncurated packages, or your lab's methodology — against a frozen copy of the same dataset, with your approval on every run. Bigger jobs can be handed to the Analysis Workbench, which works through them in the background and reports live progress. These custom outputs are clearly labeled “User-defined” and are kept separate from the curated pipeline: they never feed certainty ratings or generated reports. See custom analysis and the Workbench for details.

Ask about your data

Each analysis has an assistant that answers questions about the review's own data: which endpoints already have extracted values and which are still missing, what has been extracted for a particular study and what is awaiting review, where the review stands overall, how the research question and endpoints are set up, and why a record was included or excluded at screening — the recorded decision, its confidence, and the abstract passages quoted against each screening question. Open it with the “Ask” button in the analysis header (or ⌘/ on a Mac, Ctrl+/ elsewhere); it stays open while you move between the pages of an analysis, and an answer keeps streaming while you do.

It is read-only. It never changes the protocol, runs an analysis, or edits a value — the configuration assistant and the Analysis tab keep those jobs — and every figure it gives is a live count over your extracted data, shown next to the answer as a card or table you can expand. On the extraction page it knows which study and endpoint you are looking at, and a row in an expanded table can take you straight to that study or endpoint; on the setup page it can open an endpoint's definition for you. Its starter suggestions come from the review itself: the endpoints with the biggest gaps, values awaiting review, or a summary of the question. The assistant is currently in controlled rollout.

Review automation and approval checkpoints

Automated review can flag missing data, stale appraisals, inconsistent units, or protocol-level questions. Data-level repairs are either confirmed by a follow-up verification check or handed to you for review — and always disclosed — while protocol-level changes pause for explicit approval. Where search drafting is enabled, the same principle applies at the very start: the workflow can draft a search strategy for you, but it pauses for your review and approval before any search runs — locking the strategy is the approval that lets the run continue.

For runs that finish with unresolved signals, an automated deep audit can additionally read the extracted data next to its source quotes and flag issues the standard checks cannot express — for example an outcome typed inconsistently with how the paper reports it. Every deep-audit finding arrives on the review page with its supporting evidence, clearly labelled as machine-suggested. Narrow, well-evidenced data fixes — reversed study groups, unit conversions, a value corrected against the quotation it came from, a targeted re-read of one study — may be repaired automatically: each automatic repair is re-checked in a follow-up verification pass and listed with the check it passed, values a reviewer has validated or edited are never overwritten, and a repair that cannot be confirmed is handed to you instead. Anything protocol-level runs only with your approval; approved re-runs update the results and are disclosed in the report like any other protocol deviation.

Living reviews

Living reviews monitor for new studies and evidence changes after a review is established. New evidence returns through screening, full-text retrieval, extraction, analysis, and reviewer approval. One-off NMA preparation is supported separately from recurring living NMA refresh.

Reports and Evidence Board

The Evidence Board lets reviewers pin plots, tables, notes, and decisions that should support a report. Generated drafts use selected evidence as a starting point; they should be edited, checked, and approved before sharing.

www.axelium.ai · Reports

Teams and collaboration

Team reviews add roles, invitations, assignments, comments, notifications, dual-review modes, conflict adjudication, and milestone sign-off. Use collaboration features when a review needs independent judgements, workload splitting, or accountable approval.

Data provenance and audit

Axelium is designed so reviewers can trace results back to studies, source documents, extracted values, reviewer decisions, and report evidence. This does not remove the need for manual verification; it makes verification easier to perform and explain.

Where a quoted value has already been located in the source text by an earlier pass, the lineage view can reuse that result instead of searching the document again. Where it cannot — because the earlier result was inconclusive, or the source has been updated since — it checks the document again.

www.axelium.ai · Analysis

FAQ