ArticleEuropean geriatric medicine2026
AI-assisted PEG aftercare education for older adults: clinician-informed chatbot design (PEGAssist).
Article in European geriatric medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- ChatGPT-4o in delirium recognition: a pilot study.Revista da Associacao Medica Brasileira (1992) · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo evaluate whether a geriatric-focused, ChatGPT-based chatbot (PEGAssist) provides clinically adequate and comprehensible guidance for percutaneous endoscopic gastrostomy (PEG) aftercare in older adults. We examined whether its answers met expert expectations for depth/clinical usefulness, clarity/actionability, and scientific accuracy, with emphasis on complication recognition and triage.
methodsA multidisciplinary panel (geriatrics, nursing, surgery) independently rated chatbot responses to a curated set of common PEG aftercare questions spanning education, routine care, troubleshooting, and complications. Ratings addressed depth, clarity, and accuracy; inter-rater reliability was calculated. Free-text comments were analyzed to identify safety-critical omissions and practical improvements.
resultsOverall answer quality was considered clinically appropriate, with good inter-rater agreement. Performance was strongest in complication management, where responses consistently highlighted clear red-flag signs (e.g., infection, tube dislodgement, and persistent pain) and specified escalation pathways (self-care, 24-48 h contact, urgent evaluation). No unsafe recommendations were identified. Needed refinements included frailty-aware tailoring and more stepwise, caregiver-oriented instructions.
conclusionsA geriatric-focused LLM chatbot can deliver clinically useful, understandable PEG aftercare guidance aligned with expert expectations, particularly for recognizing complications and directing timely care. This clinician-informed evaluation assessed expert perceptions of chatbot responses; patient or caregiver usability and comprehension were not examined in this phase. Integrating such tools into discharge education may enhance safety and caregiver confidence. Prospective usability and effectiveness studies in older adults and caregivers are warranted.
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Registered trials
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