Empowering Evidence-Based Science

The Medical Research Workflow Engine

Accelerate your clinical discovery from hypothesis to publication. Integrated AI workflows designed for researchers who demand precision, evidence, and speed.

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Research Discovery

Intelligent topic selection and evidence gap analysis. Identify high-impact research areas with clinical significance.

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Review Builder

Generate structured literature reviews with AMA/APA citations. 100% real references, verified against PubMed and Scholar.

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Writing Co-pilot

Step-by-step drafting assistance. Optimize medical terminology and academic flow without compromising authorship integrity.

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Submission Assistant

Advanced journal matching based on Impact Factor, acceptance rates, and review speed. Tailored for clinical scientists.

Step 01

Zero-Hallucination Discovery

Tired of AI making up papers? Our Discovery module connects directly to PubMed and Semantic Scholar APIs to ensure every piece of evidence is real and traceable.

Automated PICO extraction
Evidence level assessment (RCT / Meta-analysis)
Research gap identification

Discovery Workflow

Step 02

Structured Review Engine

Build comprehensive literature reviews in hours, not months. Our step-by-step workflow ensures logical structure and rigorous documentation.

Thematic organization of findings
Automatic AMA/APA citation generation
Comparison tables for study outcomes

Review Engine

Research method series

Causal inference and clinical trial design

A connected guide to causal methods, sequential decisions, and evidence generation for medical researchers.

Target trial emulation Regression discontinuity Instrumental variables Marginal structural models Group-sequential trials Bayesian adaptive trials Dynamic treatment regimes

Latest Insights

Scientific Methodology Blog

Transporting Treatment Effects from Randomized Trials to Target Populations

Transporting Treatment Effects from Randomized Trials to Target Populations

Randomized trials estimate internally valid effects, but the target-population effect may differ. Learn how to define effect modifiers, assess positivity, and use standardization or sampling weights.

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Fairness and Subgroup Performance in Clinical Prediction Models

Fairness and Subgroup Performance in Clinical Prediction Models

One pooled AUC can conceal clinically important disparities. Learn how to assess subgroup calibration, discrimination, error rates, thresholds, and net benefit.

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Internal-External Cross-Validation for Clinical Prediction Models

Internal-External Cross-Validation for Clinical Prediction Models

Test model generalizability across hospitals, studies, regions, or time periods by leaving one meaningful cluster out at a time and quantifying heterogeneity.

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Clinical Prediction Model Updating and Recalibration

Clinical Prediction Model Updating: Recalibration, Revision, and Extension

External validation shows where a model fails; updating determines how to repair it. Learn when to use recalibration, revision, predictor extension, shrinkage, and independent validation.

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External Validation of Clinical Prediction Models

External Validation and Transportability of Clinical Prediction Models

A model that performs well in development data is not automatically transportable. Learn how to assess discrimination, calibration, clinical utility, recalibration, and external validity.

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Handling Missing Data in Clinical Trials

Handling Missing Data in Clinical Trials: Advanced Imputation and Sensitivity Analysis

Missing data analysis starts with the estimand. Learn MCAR, MAR, MNAR, multiple imputation, pattern-mixture models, delta adjustment, and sensitivity analysis for regulatory-grade trials.

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