Dataset on patient education and digital information quality in knee cartilage restoration with matrix-induced autologous chondrocyte implantation (MACI)

TL;DR

This paper presents a dataset of online questions and websites related to MACI, a treatment for knee cartilage restoration, to help evaluate and improve patient education resources.

Contribution

The paper introduces a curated dataset of patient-related online questions and websites about MACI, along with classification frameworks and credibility scores.

Findings

The dataset includes 1107 relevant question–website pairs with credibility scores and thematic groupings.

Classification frameworks and frequently repeated questions are provided to guide the development of educational content.

Descriptive statistics and logistic regression were used to analyze the dataset's characteristics.

Abstract

This dataset provides a comprehensive collection and classification of publicly available online questions and linked websites related to matrix-induced autologous chondrocyte implantation (MACI), an implant that can be utilized by orthopaedic surgeons for patients requiring knee cartilage restoration. Eight MACI-related search terms were entered individually into a history-cleared Google Chrome browser in incognito mode to minimize personalization bias. For each term, the “People Also Ask” feature was expanded to retrieve approximately 200 question-website pairs, yielding a total of 1620 entries that were compiled and screened for relevance. The final dataset includes 1107 unique, relevant question–website pairs organized in a spreadsheet containing variables for search term, question text, linked website, website source type, Rothwell classification (Fact, Policy, or Value) and subcategories, JAMA Benchmark Criteria component scores, total JAMA credibility score, and thematic grouping based on question content and author consensus. Each entry was rated independently by two reviewers, with discrepancies resolved by the primary author using an Excel-based verification process. Descriptive statistics and logistic regression were performed in Python (statsmodels, SciPy). The dataset is accompanied by materials outlining classification frameworks, frequently repeated questions, and commonly linked websites. By documenting how patients search for and encounter information on a popular cartilage restoration option, this dataset provides a model for evaluating digital health resources and developing accurate, accessible educational content for patients and clinicians across medical disciplines.

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