Evidence Synthesis

DeepCog's Evidence Synthesis pipeline automates the systematic review process end-to-end, following PRISMA 2020 guidelines from search strategy through final reporting. The system ingests literature from PubMed, Embase, Cochrane, and other major databases, applies configurable inclusion/exclusion criteria, and tracks the full screening flow for auditability and reproducibility.

• Automated screening — title/abstract and full-text screening against pre-specified criteria, with conflict flagging for human review.
• Quality assessment — study-level risk-of-bias evaluation using Cochrane RoB 2, ROBINS-I, or Newcastle-Ottawa scales, plus GRADE certainty ratings for the body of evidence.
• Heterogeneity analysis — I² and Q-statistic computation, subgroup and sensitivity analyses to identify sources of variation across studies.
• Meta-analytic modeling — fixed-effect and random-effects pooling (DerSimonian-Laird, REML), with meta-regression for exploring moderator effects.
• Forest plot generation — publication-ready forest and funnel plots with effect sizes, confidence intervals, and weighting displayed automatically.
• PRISMA flow diagrams — auto-generated study identification/screening/inclusion flowcharts for manuscript submission.
• Structured reporting — draft evidence tables and results sections formatted to PRISMA/MOOSE reporting standards.

• Rolling model updates — underlying extraction models are retrained as new literature and reporting conventions emerge, keeping extraction accuracy current.
• Human-in-the-loop calibration — reviewer corrections feed back into training data, tightening model weights over successive review cycles.
• Full auditability — every extracted data point is traceable to its source passage and the model/version that produced it.