Evidence map›Paper›PMID 39830641›Full record

ArticleFrontiers in digital health2024

Enhancing patient education on the role of tibial osteotomy in the management of knee osteoarthritis using a customized ChatGPT: a readability and quality assessment.

Stephen Fahy, Stephan Oehme, Danko Dan Milinkovic, Benjamin Bartek

Abstract read
In one paragraph

Article in Frontiers in digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Stephen FahyCentrum für Muskuloskeletale Chirurgie, Charité Universitätsmedizin Berlin, Berlin, Germany.
Stephan OehmeCentrum für Muskuloskeletale Chirurgie, Charité Universitätsmedizin Berlin, Berlin, Germany.
Danko Dan MilinkovicCentrum für Muskuloskeletale Chirurgie, Charité Universitätsmedizin Berlin, Berlin, Germany.
Benjamin BartekCentrum für Muskuloskeletale Chirurgie, Charité Universitätsmedizin Berlin, Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Knee osteoarthritis (OA) significantly impacts the quality of life of those afflicted, with many patients eventually requiring surgical intervention. While Total Knee Arthroplasty (TKA) is common, it may not be suitable for younger patients with unicompartmental OA, who might benefit more from High Tibial Osteotomy (HTO). Effective patient education is crucial for informed decision-making, yet most online health information has been found to be too complex for the average patient to understand. AI tools like ChatGPT may offer a solution, but their outputs often exceed the public's literacy level. This study assessed whether a customised ChatGPT could be utilized to improve readability and source accuracy in patient education on Knee OA and tibial osteotomy. Methods: Commonly asked questions about HTO were gathered using Google's "People Also Asked" feature and formatted to an 8th-grade reading level. Two ChatGPT-4 models were compared: a native version and a fine-tuned model ("The Knee Guide") optimized for readability and source citation through Instruction-Based Fine-Tuning (IBFT) and Reinforcement Learning from Human Feedback (RLHF). The responses were evaluated for quality using the DISCERN criteria and readability using the Flesch Reading Ease Score (FRES) and Flesch-Kincaid Grade Level (FKGL). Results: The native ChatGPT-4 model scored a mean DISCERN score of 38.41 (range 25-46), indicating poor quality, while "The Knee Guide" scored 45.9 (range 33-66), indicating moderate quality. Cronbach's Alpha was 0.86, indicating good interrater reliability. "The Knee Guide" achieved better readability with a mean FKGL of 8.2 (range 5-10.7, ±1.42) and a mean FRES of 60 (range 47-76, ±7.83), compared to the native model's FKGL of 13.9 (range 11-16, ±1.39) and FRES of 32 (range 14-47, ±8.3). These differences were statistically significant ( Conclusions: Fine-tuning ChatGPT significantly improved the readability and quality of HTO-related information. "The Knee Guide" demonstrated the potential of customized AI tools in enhancing patient education by making complex medical information more accessible and understandable.

Indexed as

artificial intelligenceChatGPTDISCERN criteriahigh tibial osteotomyknee osteoarthritispatient educationreadability

Identifiers

PMID39830641
PMCPMC11738919

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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.