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Enrolling by invitation NCT07724327

A Simulated Case Study of a Peritoneal Dialysis-Specialized Large Language Model Assisting Doctors in Improving Decision-Making in Peritoneal Dialysis Management

No phase Interventional Peritoneal Dialysis (PD) Large Language Models

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Peritoneal dialysis-specialized large language model..
Who it may be relevant to
Registry conditions: Peritoneal Dialysis (PD), Large Language Models. Basic parameters: No limits · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Official title

Generative AI-Assisted Understanding and Decision Enhancement in Peritoneal Dialysis: A Randomized Controlled Trial (The GUIDE-PD Trial)

Overview

This study is a randomized controlled trial based on simulated clinical cases, aiming to establish a standardized evaluation system for PD physicians, to assess the differences in PD management quality between a workflow assisted by a PD-specialized large language model and physician-only decision-making, and to identify potential risks (such as generating obviously erroneous or even harmful recommendations). This simulated clinical case framework not only supports standardized and blinded evaluation, but also provides preliminary evidence for the model's effectiveness and safety before its deployment in real clinical settings, while avoiding direct impact on real patients.

Interventions

  • Other Peritoneal dialysis-specialized large language model.
    The peritoneal dialysis-specialized large language model (PD-LLM) used in this study was jointly developed by the Department of Nephrology at the First Affiliated Hospital of Sun Yat-sen University and Digital Health China (DHC).

Primary outcome measures

  • Management Reasoning [Time frame: Within the 120-minute test period of the first test.]
Secondary outcome measures (5)
  • Domain-Specific Scores [Time frame: Within the 120-minute test period of the first test.]
  • Severity of Potential Harm [Time frame: Within the 120-minute test period of the first test.]
  • Time per Scenario Case [Time frame: Within the 120-minute test period of the first test.]
  • Self-Reported Confidence per Case [Time frame: Within the 120-minute test period of the first test.]
  • Difference between the Phys group's scores when tested with PD-LLM assistance and when assisted only by traditional search methods [Time frame: After 1-2 months of washout of first test]

Eligibility criteria

Inclusion criteria

  • Internal medicine or nephrology standardized training residents, licensed residents, or attending physicians.
  • Independent experience in PD management ≤ 3 years.
  • Provided signed informed consent and agreed to comply with the trial procedures.

Exclusion criteria

  • Direct involvement in the development or training of the specialized PD large language model, or in the construction of the clinical scenarios/ scoring criteria used in this trial.
  • Participation in the pilot testing of all clinical scenarios used in this trial.
  • Inability or unwillingness to access the study platform or use online resources during the study period.
  • Experienced PD experts.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation
Randomized
Model
Parallel assignment
Masking
Single blind
Primary purpose
Treatment

Study locations

China · 1 center
  • The First Affiliated Hospital of Sun Yat-sen University — Guangzhou

Identifiers

NCT: NCT07724327 · IIT-2026-511

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗