Introducing ActiveTLR: targeted literature reviews in hours, not weeks

Targeted literature reviews are the backbone of regulatory, HEOR, and medical-affairs evidence. Yet most of the work is still done by hand: crafting Boolean strings, de-duplicating exports, and screening thousands of abstracts one at a time. It is slow, hard to reproduce, and unforgiving of a single missed paper.

We built ActiveTLR because that process deserved better tooling — not a shortcut that hides the method, but software that performs each step in the open and hands the reviewer a clear, auditable trail.

From a question to a synthesis

ActiveTLR breaks a targeted review into transparent, reviewable steps:

• Ask the question. Type it in plain language; ActiveTLR identifies the question type and maps it to the right framework — PICOS, PICOC, PEO, PFO, or CoCoPop.
• Search strategy. It drafts a PubMed search from your framework and inclusion criteria, which you review and edit before it runs.
• De-duplication. Records from PubMed and any imported RIS files are merged and de-duplicated so the same study is never screened twice.
• Screening. AI classifies each title and abstract against your criteria, with a stated rationale — and every decision is yours to override.
• Extraction and synthesis. Structured data extraction and a draft narrative synthesis, produced from the actual retrieved records.

Transparent by design

A human reviewer stays in control at every decision point. ActiveTLR keeps an auditable trail — the framework and inclusion criteria, the search strategy, import and de-duplication counts, every screening decision and its rationale, the PRISMA flow diagram, and exportable record-level data — designed to withstand scrutiny.

The result: reviews that move at the speed your team needs, without trading away the rigour a regulator expects.

Want to see it on your own research question? Book a live demo.