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Accuracy and reliability of data extraction for systematic reviews using large language models: A protocol for a prospective study

Accuracy and reliability of data extraction for systematic reviews using large language models: A protocol for a prospective study - Accuracy and reliability of data extraction for systematic reviews using large language models: A protocol for a prospective study

Status
Active, not recruiting
Phases
Unknown
Study type
Unknown
Source
JPRN
Registry ID
JPRN-UMIN000054461
Enrollment
Unknown
Registered
2024-05-23
Start date
2024-05-23
Completion date
Unknown
Last updated
2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Sepsis

Interventions

None listed

Sponsors

Chiba University Graduate School of Medicine
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Not applicable

Exclusion criteria

Exclusion criteria: Not applicable

Design outcomes

Primary

MeasureTime frame
To evaluate the accuracy and reliability of three large language models and optimize their command prompts to enhance accuracy.—

Secondary

MeasureTime frame
Detailed characteristics of errors Time to complete each task Following the primary analysis, we will optimize the original command with integration of prompt engineering techniques in the secondary analysis.—

Countries

Japan

Contacts

Public ContactTakehiko Oami

Chiba University Graduate School of Medicine Department of Emergency and Critical Care Medicine

seveneleven711thanks39@msn.com0432227171

Outcome results

None listed

Source: JPRN (via WHO ICTRP) · Data processed: Jul 3, 2026