Quality and Readability of Web-Based Information for Patients With Pancreatic Cysts: DISCERN and Readability Test Analysis
Bibliographic record
Abstract
BACKGROUND: Pancreatic cysts are a complex medical problem with several treatment options. Patients use web-based health information to understand their conditions and to guide treatment choices. OBJECTIVE: The goal of this study was to describe the quality and readability of publicly available web-based information on pancreatic cysts and to compare this information across website affiliations. METHODS: A Google search for "pancreatic cysts" was performed and the first 30 websites were evaluated. Website affiliations were classified as academic, media, nonprofit, government, or not disclosed. Information describing cancer risk was recorded. The DISCERN instrument measured the quality of content regarding treatment choices. Four standardized tests were used to measure readability. RESULTS: Twenty-one websites were included. The majority of the websites (20/21, 95%) described the cancer risk associated with pancreatic cysts. Nearly half of the websites were written by an academic hospital or organization. The average DISCERN score for all websites was 40.4 (range 26-65.5, maximum 80). Websites received low scores due to lack of references, failure to describe the risks of treatment, or lack of details on how treatment choices affect quality of life. The average readability score was 14.74 (range 5.76-23.85, maximum 19+), indicating a college reading level. There were no significant differences across website affiliation groups. CONCLUSIONS: Web-based information for patients with pancreatic cysts is of moderate quality and is written above the reading level of most Americans. Gastroenterological, cancer treatment organizations, and physicians should advocate for improving the available information by providing cancer risk stratification, treatment impact on quality of life, references, and better readability.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".