AI peer review for epidemiology and population science

PerfectPaper reviews population science manuscripts with a standing team of specialist agents, and lets you add up to three custom agents for the conventions that team does not cover. This page contains five prebuilt agent briefs for cancer control and population sciences: health equity reporting, competing risks and time-related bias, registry and claims data validity, causal language discipline, and qualitative rigour. Each is a text brief you copy into the agent field.

Two of these fill gaps that are unusually wide. The standing team has no qualitative competence at all, since it was built for quantitative manuscripts. And nothing in a general review is briefed on how race, ethnicity and socioeconomic position should be reported — which is the single most consequential reporting question in disparities research.

The five agents

Health equity reporting — race and ethnicity treated as biological rather than social, category derivation unstated, area-level measures read as individual-level, disparities documented without mechanism.

Competing risks and time-related bias — Kaplan-Meier where a competing risks method is required, immortal time in registry and claims cohorts, lead-time and length-time bias in screening studies.

Registry and claims data validity — SEER, NCDB and Medicare coding limitations glossed over, missing stage or treatment detail, conclusions the data source cannot support.

Causal language discipline — observational findings written in causal verbs, adjustment sets unjustified, no DAG.

Qualitative and mixed-methods rigour — saturation claimed without evidence, coding reliability, reflexivity, member checking.

How custom agents work

You can add up to three custom agents to a review. Each takes a name, instructions up to 6,000 characters, a work type, a skill level, and an optional tool selection. PerfectPaper supplies the review contract and output format, so the brief describes what to look for.

Choosing among them

For a survival analysis in a cancer cohort, competing risks earns a slot before anything else, because death from other causes is common in these populations and the wrong method biases every reported probability upward. For a disparities paper, health equity reporting comes first. For any observational study, causal language is a cheap and high-yield slot. Registry data validity suits secondary analyses of SEER, NCDB or claims. Qualitative rigour is essential for interview and focus group work and irrelevant elsewhere.

There is deliberate overlap with the standing team on causal inference; the causal language agent is scoped to the claims-and-wording layer for that reason.

Review my manuscript

Frequently asked questions

How many custom agents can I add to one review?

Three. They run alongside the standing specialist team, which already covers statistical models, causal inference and confounding, survey design, tables and citations.

Does PerfectPaper review qualitative research?

The standing team is built for quantitative manuscripts, so qualitative work is the clearest case for a custom agent. The qualitative rigour brief covers saturation, coding reliability, reflexivity and member checking.

Which agent should I use for a SEER analysis?

Registry data validity, paired with competing risks. Cancer registry survival analyses routinely need both, since registry data have known coding limits and cancer cohorts have substantial competing mortality.

Do these agents overlap with the built-in causal inference review?

The causal language agent does, deliberately. It is scoped to how claims are worded rather than to identification strategy, which the standing specialist already covers.