A Systematic Review of Patient Inflammatory Bowel Disease Information Resources on the World Wide Web
Bibliographic record
Abstract
BACKGROUND AND AIMS: The Internet is a widely used information resource for patients with inflammatory bowel disease, but there is variation in the quality of Web sites that have patient information regarding Crohn's disease and ulcerative colitis. The purpose of the current study is to systematically evaluate the quality of these Web sites. METHODS: The top 50 Web sites appearing in Google using the terms "Crohn's disease" or "ulcerative colitis" were included in the study. Web sites were evaluated using a (a) Quality Evaluation Instrument (QEI) that awarded Web sites points (0-107) for specific information on various aspects of inflammatory bowel disease, (b) a five-point Global Quality Score (GQS), (c) two reading grade level scores, and (d) a six-point integrity score. RESULTS: Thirty-four Web sites met the inclusion criteria, 16 Web sites were excluded because they were portals or non-IBD oriented. The median QEI score was 57 with five Web sites scoring higher than 75 points. The median Global Quality Score was 2.0 with five Web sites achieving scores of 4 or 5. The average reading grade level score was 11.2. The median integrity score was 3.0. CONCLUSIONS: There is marked variation in the quality of the Web sites containing information on Crohn's disease and ulcerative colitis. Many Web sites suffered from poor quality but there were five high-scoring Web sites.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".