Peritoneal Dialysis (PD) Specialized LLM for PD Management

NCT07539428 — Not applicable, Enrolling by invitation, 150 participants, First Affiliated Hospital, Sun Yat-Sen University

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Physician Implementation of an NLP-Empowered Electronic Resource for Peritoneal Dialysis Management: A Randomized Controlled Trial (The PIONEER-PD Trial)

Registry	NCT07539428, acronym PIONEER-PD
Status	Enrolling by invitation
Phase	Not applicable
Study type	Interventional; randomized, parallel, masking: single, treatment
Enrollment	150 (Estimated)
Sponsor	First Affiliated Hospital, Sun Yat-Sen University (Other); collaborators: Vantive Health LLC
Conditions	Peritoneal Dialysis (PD), Large Language Models
Interventions	Peritoneal dialysis-specialized large language model., Generalized LLM tools such as deepseek (Other)
Dates	start 2026-04-05, primary completion 2026-05-30 (estimated), completion 2026-05-30 (estimated), first posted 2026-04-20, last updated 2026-04-20
Locations	1 site in China

-Summary

This study conducted a randomized controlled trial using clinical vignettes to evaluate differences in the quality of PD management among two large language model-assisted workflows and a physician-only decision-making process, and to identify potential risks (e.g., generating clearly erroneous or even harmful suggestions).

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+Arms and interventions

Arm	Type	Interventions	Description
Phys Group	No intervention		Participants could search for relevant knowledge, but access to large language model (LLM)-related websites was prohibited.
Phys+DS Group	Placebo comparator	Other: Generalized LLM tools such as deepseek	Participants had access to both the web and the DeepSeek interface.
Phys+PD-LLM Group	Experimental	Other: Peritoneal dialysis-specialized large language model.	Participants had access to the web and a specialized PD-LLM provided by the First Affiliated Hospital of Sun Yat-sen University.

Peritoneal dialysis-specialized large language model. (Other): 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).
Generalized LLM tools such as deepseek (Other): Participants were permitted to ask the LLM any questions related to the clinical scenarios. However, they were explicitly instructed to critically evaluate the model's suggestions and to take full responsibility for the final clinical plans.

+Outcome measures

Primary outcomes

Management Reasoning [Within 90-min study]: Percent correct (range: 0 to 100) for each case.

Secondary outcomes

Domain-Specific Scores [within 90-min study.]: Percent correct (range: 0 to 100) for each case.
Severity of Potential Harm [Within 90-min study.]: The severity of potential harm will be classified as none, mild-to-moderate, or severe.
Time Spent on Management [Within 90-min study.]: The time participants spend per case.
Self-Reported Confidence per Case [Within 90-min study.]: Scale 1-10. 10 represents being very confident.

Other outcomes

Total vignette performance score of LLM generated answers [Within 90-min study.]: Percent correct (range: 0 to 100) for each case.
Severity of Potential Harm of LLM-generated answers [Within 90-min study.]: The severity of potential harm will be classified as none, mild-to-moderate, or severe.

+Eligibility

Sex: All

Inclusion Criteria:

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

Exclusion Criteria:

1. 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.
2. Participation in the pilot testing of all clinical scenarios used in this trial.
3. Inability or unwillingness to access the study platform or use online resources during the study period.
4. Experienced PD experts.

+Locations (1)

Facility	City	Country	Status
Sixiu Chen	Guangzhou, Guangdong	China

My Notes

Public

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