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<p>Phytotherapy is widely used by cancer patients as a complementary and alternative medicine approach. With the increasing reliance on the internet for health-related information, concerns regarding the quality, reliability, and readability of online phytotherapy content have become more prominent. This study aimed to evaluate the readability and quality of web-based information on phytotherapy for cancer patients using validated assessment tools and to identify specific deficiencies in content quality. A descriptive cross-sectional analysis was conducted using the Google search engine with four predefined search terms related to phytotherapy and oncology. The first 50 websites for each term were screened, yielding 200 websites, of which 99 met the inclusion criteria. Websites were categorized by source type and visibility. Readability was assessed using the Flesch–Kincaid Grade Level (FKGL), Gunning Fog Index, SMOG, and Coleman–Liau Index. Content quality was evaluated using the JAMA benchmark criteria and the DISCERN instrument, including item-level analysis. Non-parametric statistical tests were applied where appropriate. The median FKGL score was 9.3, indicating that most content required a high reading level. The median JAMA score was 4, while the median DISCERN score was 55, reflecting moderate but variable quality. Item-level analysis revealed that critical aspects such as treatment risks, benefits, uncertainties, and consequences of no treatment were frequently insufficiently addressed. Commercial websites demonstrated lower DISCERN scores compared with non-commercial sources. No significant differences were observed between first-page and subsequent search results. Online phytotherapy information for cancer patients is characterized by moderate quality, high readability demands, and important deficiencies in key domains necessary for informed decision-making. In the evolving landscape of AI-assisted health information retrieval, these limitations may have broader implications, highlighting the need for accurate, evidence-based, and accessible online resources.</p>

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Evaluation of Web-based Information on Phytotherapy for Cancer Patients: A Quality and Readability Analysis

  • 期刊论文

  • 发表日期：

    2026年6月18日

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  • Mehmet Uzun,
  • Savas Gokcek,
  • Erhan Kaya,
  • Muhammed Semih Gedik

摘要

Phytotherapy is widely used by cancer patients as a complementary and alternative medicine approach. With the increasing reliance on the internet for health-related information, concerns regarding the quality, reliability, and readability of online phytotherapy content have become more prominent. This study aimed to evaluate the readability and quality of web-based information on phytotherapy for cancer patients using validated assessment tools and to identify specific deficiencies in content quality. A descriptive cross-sectional analysis was conducted using the Google search engine with four predefined search terms related to phytotherapy and oncology. The first 50 websites for each term were screened, yielding 200 websites, of which 99 met the inclusion criteria. Websites were categorized by source type and visibility. Readability was assessed using the Flesch–Kincaid Grade Level (FKGL), Gunning Fog Index, SMOG, and Coleman–Liau Index. Content quality was evaluated using the JAMA benchmark criteria and the DISCERN instrument, including item-level analysis. Non-parametric statistical tests were applied where appropriate. The median FKGL score was 9.3, indicating that most content required a high reading level. The median JAMA score was 4, while the median DISCERN score was 55, reflecting moderate but variable quality. Item-level analysis revealed that critical aspects such as treatment risks, benefits, uncertainties, and consequences of no treatment were frequently insufficiently addressed. Commercial websites demonstrated lower DISCERN scores compared with non-commercial sources. No significant differences were observed between first-page and subsequent search results. Online phytotherapy information for cancer patients is characterized by moderate quality, high readability demands, and important deficiencies in key domains necessary for informed decision-making. In the evolving landscape of AI-assisted health information retrieval, these limitations may have broader implications, highlighting the need for accurate, evidence-based, and accessible online resources.