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Record W2120301443 · doi:10.1002/ibd.22834

Internet and electronic resources for inflammatory bowel disease: A primer for providers and patients

2011· review· en· W2120301443 on OpenAlexafffund
Kyle J. Fortinsky, Marc R. Fournier, Eric I. Benchimol

Bibliographic record

VenueInflammatory Bowel Diseases · 2011
Typereview
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsMisinformationSocial mediaInflammatory bowel diseaseMedicineThe InternetInternet privacyGeneral partnershipHealth careDiseaseBusinessWorld Wide WebComputer scienceInternal medicinePolitical science

Abstract

fetched live from OpenAlex

Patients with inflammatory bowel disease (IBD) are increasingly turning to the Internet to research their condition and engage in discourse on their experiences. This has resulted in new dynamics in the relationship between providers and their patients, with misinformation and advertising potentially presenting barriers to the cooperative patient-provider partnership. This article addresses important issues of online IBD-related health information and social media activity, such as quality, reliability, objectivity, and privacy. We reviewed the medical literature on the quality of online information provided to IBD patients, and summarized the most commonly accessed Websites related to IBD. We also assessed the activity on popular social media sites (such as Facebook, Twitter, and YouTube), and evaluated currently available applications for use by IBD patients and providers on mobile phones and tablets. Through our review of the literature and currently available resources, we developed a list of recommended online resources to strengthen patient participation in their care by providing reliable, comprehensive educational material.

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)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.349
Teacher spread0.295 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations53
Published2011
Admission routes2
Has abstractyes

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