Biologic agents in inflammatory bowel disease – quality of internet website information
Bibliographic record
Abstract
BACKGROUND/AIMS: Many patients currently seek the Internet for health-related information without discerning the quality or bias of the evidence presented. Biologic agents have become the mainstay of therapy in inflammatory bowel disease (IBD), and it is important that patients have access to high-quality information when exploring the various available agents to make informed decisions about their therapy. The primary aim of this study was to evaluate the quality of patient-searched Internet websites that describe the biologic agents used as treatment options for IBD. The secondary aim was to compare the quality of patient-searched with physician-recommended websites and to evaluate any differences. MATERIALS AND METHODS: The DISCERN model was used to evaluate the quality of the information content of a total of 110 websites of all the biologic agents used in the treatment of IBD from July to September 2017. The first 10 "Google search" hits meeting the inclusion criteria for each agent were included. There were four additional physician-recommended websites that were evaluated for the purpose of the secondary aim of this study. RESULTS: The mean DISCERN score among all websites combined was 3.21 out of a 5-point scale. The highest scoring website was "ema.europa.eu" at 4.13 whereas the lowest scoring website was "https://www.fda.gov" at 1.97 for Entyvio. There was no significant difference between patient-searched and physician-recommended websites, with a mean total score of 3.21 versus 3.63, respectively (P value of 0.158). CONCLUSIONS: The combined quality of Internet web-based resources used for each drug was fairly consistent in scoring (intermediate to slightly above average). There was no significant advantage in the overall combined scores of the pooled physician-recommended websites when compared with the patient-searched websites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".