ArticleFrontiers in public health2025
Evaluating the effectiveness and limitations of online health information tools in assessing the quality of medication-related content.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Quality and reliability of short videos about long COVID on bilibili and tiktok: cross-sectional study under health community construction background in China.BMC public health · 2026Article
- #infertility education on Instagram: A Reliability Analysis in Brazil Using the DISCERN Instrument.JBRA assisted reproduction · 2026Article
- Nationwide assessment of community pharmacists' practices and atorvastatin-drug interactions in Egypt.Scientific reports · 2026Article
- The quality and reliability of short videos about premature ovarian failure on Bilibili and TikTok: Cross-sectional study.Digital healthArticle
- A cross-sectional study of the quality and reliability of renal cell carcinoma-related short videos on Bilibili and TikTok.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The internet has become a primary source of information on medicines, yet the quality of this information is inconsistent. Despite the proliferation of web-based resources, limited research has specifically examined the reliability of online information on medicines. The variability in quality can be attributed to the recent shift toward digital information-seeking and the absence of specialized tools designed to assess the quality of medication-related information online. Aim: To evaluate the quality of information about Amitriptyline available on websites using three well-established, generic evaluation tools-HONcode, DISCERN, and JAMA-compared against the domain-specific Keystone action plan criteria. Methods: Adopting an evaluative research design, this study utilized the aforementioned four assessment tools to collect and analyze data. Four independent assessors were recruited to evaluate the quality of the information on selected websites. Results: A Google search using "Amitriptyline" identified 30 websites for the study sample. Reliability analysis using Cronbach's alpha, the Inter-Class Correlation coefficient, and Kendall's rank-order correlation coefficient revealed that the Keystone criteria demonstrated the highest internal consistency and reliability compared to DISCERN, HONcode, and JAMA. Conclusion: This study indicates that the Keystone action plan criteria may serve as a reliable, domain-specific tool for evaluating medicines information on websites. The results underscore the importance of specialized assessment criteria for online medication information, as they provide more consistent and accurate evaluations compared to generic tools.
Indexed as
Identifiers
What OpenQuestion holds
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.