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Axelium
Existing users
For clinicians & guideline committees

Recommendations that move when the evidence moves.

Living reviews flag the day a pooled estimate moves, so recommendations stay aligned with the evidence — and every effect estimate links back to the study it came from.

Read the validation case study
Living reviews · treatment networks · source-linked estimates
www.axelium.ai · Forest plotLive
Random-effects pool · primary outcome6 studies
Smith 2024
0.62 [0.41, 0.94]
Chen 2023
0.71 [0.55, 0.92]
Patel 2024
0.83 [0.62, 1.11]
Garcia 2023
0.74 [0.58, 0.95]
Okafor 2024
0.88 [0.71, 1.09]
Müller 2022
1.05 [0.78, 1.41]
Pooled (RE)
0.78 [0.65, 0.93]
← favours interventionfavours control →
study estimate (size ∝ weight)pooled (RE)
RR, log scale
The shift

From guidelines that age to evidence that keeps pace.

Same workflow, two states. Flip the switch to see what changes.

Reviewed once · then it ages Live · re-runs as trials are indexed
The evidence baseFrozen at publicationRe-runs as new trials are indexedfrozenliving
New RCTsNoticed at the next scheduled reviewFlagged the day they're indexed~yearsdays
The recommendationAges with the guidelineMoves when the evidence movesstaticcurrent
Static — the evidence moves on without itTracking the pooled estimate as it changes
How it works

The evidence base, kept current for you.

Build the review once, let it re-run as new trials are indexed, and inspect every estimate back to its source.

Build

Build the comparison once

  • A systematic review and a network meta-analysis
  • Anchored against every relevant comparator
  • Defensible from the first submission
Keep live

Re-runs on a schedule

  • The whole pipeline re-runs on a cadence
  • Flags when a new trial moves the result
  • A signed record of what changed, each cycle
Inspect

No black box

  • Deterministic statistics — same inputs, same result
  • Every estimate traces to its source study
  • Change an assumption and re-run
Why trust it

Conclusions that hold up in peer review.

Validation

We reproduced a published forest plot — exactly.

In our reproduction case study, Axelium rebuilt a published meta-analysis end to end and matched its pooled estimate. Same inputs, same numbers — because the statistical engine is deterministic.

Comparisons a committee will accept

Pairwise and network meta-analysis, plus population-adjusted indirect comparisons, with certainty grading — every step inspectable.

Network meta-analysisMAICSTCML-NMRGRADE certainty
✦

Keep every recommendation aligned with the evidence behind it.

Read quickstart
Questions

The details.

A completed review re-runs on a schedule and flags the day a pooled estimate moves, so the committee learns a recommendation may need revisiting as soon as the evidence changes.