How Well Does the Internet Answer Patients’ Questions about Inflammatory Bowel Disease?
Bibliographic record
Abstract
BACKGROUND: the Internet is an increasingly important source of health information. OBJECTIVE: to assess how well common websites answered patients' questions regarding inflammatory bowel disease (IBD). METHODS: thirty websites were identified and evaluated. Based on a previous survey of patient information needs, a comprehensive question list was developed in the three following areas: medical information (seven items), medical treatment (six items) and selfmanagement (eight items). The websites were evaluated for the amount of information they provided to answer each question using two standard measures of information quality - the DISCERN and the Ensuring Quality Information for Patients scales. RESULTS: four particularly strong websites, scoring highest (on a scale from 1 to 5) in terms of IBD information, were the Crohn's and Colitis Foundation of America (mean information score 4.3), About.com (4.2), HealthCentral (3.8) and WebMD (3.8). These websites also scored well on the DISCERN and the Ensuring Quality Information for Patients quality scales. Most websites provided at least adequate information on common symptoms, complications, treatments and what is known (or not known) about the causes of IBD. However, many websites did not provide adequate information about prognosis, possible side effects of treatment and risks of developing cancer. Information regarding self-management was covered to a very limited extent. CONCLUSION: websites could be strengthened by providing more of the information patients deem to be important, and by more clearly identifying sources of information and the date the information was updated. Most websites would benefit from more attention given to reducing the reading level and improving the organization of 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 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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".