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Record W2148181723 · doi:10.2196/ijmr.2010

Can Consumers Trust Web-Based Information About Celiac Disease? Accuracy, Comprehensiveness, Transparency, and Readability of Information on the Internet

2012· article· en· W2148181723 on OpenAlexvenueno aff
Shawna L McNally, Michael Donohue, Kimberly P. Newton, Sandra P Ogletree, Kristen K Conner, Sarah E Ingegneri, Martin F. Kagnoff

Bibliographic record

VenueInteractive Journal of Medical Research · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
FundersUniversity of California, San DiegoWilliam K. Warren Foundation
KeywordsReadabilityTransparency (behavior)The InternetInternet privacyComputer scienceWorld Wide WebBusinessInformation retrievalComputer security

Abstract

fetched live from OpenAlex

BACKGROUND: Celiac disease is an autoimmune disease that affects approximately 1% of the US population. Disease is characterized by damage to the small intestinal lining and malabsorption of nutrients. Celiac disease is activated in genetically susceptible individuals by dietary exposure to gluten in wheat and gluten-like proteins in rye and barley. Symptoms are diverse and include gastrointestinal and extraintestinal manifestations. Treatment requires strict adherence to a gluten-free diet. The Internet is a major source of health information about celiac disease. Nonetheless, information about celiac disease that is available on various websites often is questioned by patients and other health care professionals regarding its reliability and content. OBJECTIVES: To determine the accuracy, comprehensiveness, transparency, and readability of information on 100 of the most widely accessed websites that provide information on celiac disease. METHODS: Using the search term celiac disease, we analyzed 100 of the top English-language websites published by academic, commercial, nonprofit, and other professional (nonacademic) sources for accuracy, comprehensiveness, transparency, and reading grade level. Each site was assessed independently by 3 reviewers. Website accuracy and comprehensiveness were probed independently using a set of objective core information about celiac disease. We used 19 general criteria to assess website transparency. Website readability was determined by the Flesch-Kincaid reading grade level. Results for each parameter were analyzed independently. In addition, we weighted and combined parameters to generate an overall score, termed website quality. RESULTS: We included 98 websites in the final analysis. Of these, 47 (48%) provided specific information about celiac disease that was less than 95% accurate (ie, the predetermined cut-off considered a minimum acceptable level of accuracy). Independent of whether the information posted was accurate, 51 of 98 (52%) websites contained less than 50% of the core celiac disease information that was considered important for inclusion on websites that provide general information about celiac disease. Academic websites were significantly less transparent (P = .005) than commercial websites in attributing authorship, timeliness of information, sources of information, and other important disclosures. The type of website publisher did not predict website accuracy, comprehensiveness, or overall website quality. Only 4 of 98 (4%) websites achieved an overall quality score of 80 or above, which a priori was set as the minimum score for a website to be judged trustworthy and reliable. CONCLUSIONS: The information on many websites addressing celiac disease was not sufficiently accurate, comprehensive, and transparent, or presented at an appropriate reading grade level, to be considered sufficiently trustworthy and reliable for patients, health care providers, celiac disease support groups, and the general public. This has the potential to adversely affect decision making about important aspects of celiac disease, including its appropriate and proper diagnosis, treatment, and management.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.029
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.004
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.106
GPT teacher head0.505
Teacher spread0.399 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations46
Published2012
Admission routes1
Has abstractyes

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