find-cohort-gap

To find publishable cohort gaps, convert your cohort metadata into literature gaps using saturation scanning and feasibility gating, generating ranked topic proposals with evidence of novelty.

Literature saturation scanning assesses topic novelty via PubMed searches, categorizing candidate research areas into blue, green, yellow, and red zones to identify unsaturated literature gaps.

Align cohort data with PI expertise by cross-referencing PubMed signals in an intersection matrix, matching PI strengths against cohort variables to generate high-priority, data-driven questions.

Yes, you can use cohort metadata from healthcare systems, national registries, and institutional EMRs for topic discovery, provided the data includes sample size, endpoints, and variable categories.

The 6-pattern scoring method evaluates finalist research topics against six universal patterns to rank their viability, ensuring selected literature gaps have high publishable potential.

Feasibility gating requires cohort metadata including sample size, follow-up duration, variable categories, endpoints, missing data assessment, and IRB considerations to verify research viability.