Evidence appraisal · rigor, not a recap

How solid is the evidence behind this result?

TrialReviewer doesn’t just recap a paper — it appraises the evidence like a clinical epidemiologist: recomputes absolute benefit, surfaces methodology flaws, grades quality, and shows whether the evidence supports the claim. The decision stays yours.

Includes ACCORD, SPRINT, and RECOVERY examples

Input · the abstract you paste

The ACCORD trial randomized 10,251 patients with type 2 diabetes at high CV risk to intensive glucose lowering (HbA1c<6.0%) or standard therapy… the intensive arm had higher all-cause mortality (HR 1.22, 95% CI 1.01–1.46) and was terminated early.

TrialReviewer's appraisal
Study type: RCT (early-terminated) · Evidence level: Abstract
Appraisal confidenceB
EndpointDirection
Nonfatal MIFavors intensive
CV deathFavors standard
All-cause mortalityHR 1.22 · favors standard
Severe hypoglycemia~3× excess
All-cause mortality HR 1.22 (95% CI 1.01–1.46)
1.0
The CI's lower bound (1.01) barely clears the null (1.0) — a fragile result.
Bottom Line

Intensive control did not reduce MACE; all-cause mortality rose (HR 1.22) with ~3× more severe hypoglycemia — the evidence does not support tighter glucose targets in this population.

Every threshold judgment carries an inline [Author Year] citation, e.g. [Walsh et al. 2014].

Why not just use ChatGPT?

Same number, two very different treatments. Say a trial reports a 25% relative risk reduction:

A general AI · translates

“The trial reports a 25% relative risk reduction, suggesting the intervention may reduce the outcome compared with control.”

But it does not check absolute benefit, harms, or whether the result supports the clinical claim.

TrialReviewer · appraises the evidence
  • Absolute risk reduction ARR 1.6% (not 25%)
  • Number needed to treat NNT 61
  • All-cause mortality actually increased
  • Appraisal confidence: B

Appraisal: the evidence does not support the claimed benefit.

What it does

Shows absolute benefit, not just relative risk

Doesn't parrot relative risk (RRR) — it reconstructs absolute risk reduction and number needed to treat. Absolute benefit is what the clinic acts on.

Flags hidden reasons the claim may fail

Selective reporting, immortal time bias, composite-endpoint decomposition — checked one by one, with reverse signals never ignored.

Rates evidence quality transparently

Starts from the RCT / observational baseline and states, criterion by criterion, why and how far it downgrades (GRADE-informed).

Gives a bottom-line appraisal

An A/B/C/D appraisal-confidence rating plus a one-line Bottom Line — the conclusion professionals read first.

The appraisal rests on a framework

Every appraisal is grounded in established evidence-appraisal methods and 60+ core references — not the model's intuition.

GRADE-informed evidence rating

Downgrades criterion by criterion from a high-quality start.

A/B/C/D confidence + fatal-flaw veto

A fatal flaw sends the rating straight to D.

Practice-Changing Checklist

An explicit checklist for whether the evidence clears the bar to change practice.

Benefit–Harm: ARR / NNT / NNH

Benefit and harm quantified side by side — net value.

Fragility Index

How stable the result is — how few events would flip significance.

MCID

Whether the improvement clears a clinically meaningful threshold.

Sources: Nuovo 2002 · Walsh 2014 · Balshem 2011 · Schulz 2010, and 60+ more.

Coverage

60+
core methodology references
4
study designs
Randomized controlled trials (RCT)Meta-analysis / systematic reviewCohort / observationalDiagnostic accuracy studies

Two modes — pick how you read

Ready to appraise a trial?

Start with a sample report or paste your own abstract.