Resources — Field notes, guides & methodology

Field notes

Blog / Field notes

The public record of how Axelium works

LLMs propose. Algorithms decide. Humans validate.

An architectural principle for agentic AI when outputs must be defensible. The LLM is the evidence-finder. A deterministic algorithm is the decision-maker. A human is the validator. Each role is bounded by what it is reliable for.

Axelium Engineering/14 May 2026/12 min read

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Methodology · 02LLMs propose. Algorithms decide. Humans validate.

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Can AI reproduce a published meta-analysis? We tested it.Case study · 13 April 2026

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Resources / Library

5 ITEMS

The reference library. Guides, worked examples, and methodology notes — the things you reach for while running a review, not the things we write about running one.

Open docs

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Getting startedHow-to walkthroughs

Start here

How to run your first meta-analysis in Axelium

A step-by-step guide from research question to a basic meta-analysis in the platform.

GuideOpen resource

Worked examplesEnd-to-end with data

Intervention effectiveness: a PICO example

Walk through a treatment vs standard-care question using PICO, from search planning to forest plots.

Worked exampleOpen resource

Prevalence meta-analysis: a PEO example

Estimate prevalence and risk-factor associations in a target population using the PEO framework.

Worked exampleOpen resource

MethodologyNotes & deep dives

Choosing effect measures in Axelium

How Axelium selects and computes risk ratios, odds ratios, mean differences, and more for your endpoints.

Methodology noteOpen resource

From PubMed search to forest plot in under a day

Practical tips for structuring searches and workflows to get from database exports to results quickly.

Methodology noteOpen resource