ArticleHand therapy2026
What information is available online for adults with distal radius fracture? Online literature search and synthesis using generative AI.
Article in Hand therapy, 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Distal radius fractures (DRFs) are the most common adult fracture in the UK, and patients seek online information to support recovery. The quality of such information varies, including in artificial intelligence (AI) generated summaries. This study explored the use of a generative AI model (Gemini) for information searching and synthesis of patient information on DRF recovery. Methods: Seven health information questions were co-developed with patients, hand therapists, and surgeons. Each question was searched in Google using three terms: 'distal radius fracture', 'wrist fracture', and 'broken wrist'. For each AI Overview, the summary and cited sources were exported. Citations were screened for relevance and checked for accurate representation in the AI summary. Trustworthiness was rated using a National Institutes of Health (NIH) framework. AI summaries were compared with four UK guidelines on DRF management and rehabilitation. Results: Across all searches, 277 citations were identified; 37% were irrelevant. Of relevant citations, 57% (n = 99) were highly trustworthy, 40% (n = 70) moderately, and 3% (n = 6) low. Core messages aligned with UK guidelines, but AI summaries often gave fixed recovery timeframes. Discussion: Google's AI Overview produced generally accurate, trustworthy DRF recovery information. However, variable source relevance, differing complexity by search term, and fixed timeframes highlight the need for clinicians to understand the online content patients encounter. Terminology influenced tone, 'distal radius fracture' yielded technical summaries, 'wrist fracture' mixed technical and practical advice, 'broken wrist' offered more lay-friendly guidance. Awareness of these differences can support personalised advice and bridge gaps between AI-generated content and evidence-based practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.