Quality assessment of online hepatocellular carcinoma patient education materials.
Bibliographic record
Abstract
e24184 Background: Hepatocellular carcinoma (HCC) is the fourth-leading cause of cancer-associated death in the world, claiming nearly 800,000 lives each year globally. Concurrently, an increasing number of patients are using the Internet as a source of health information. However, limited research has been done on assessing the quality of HCC websites. Therefore, we aim to systematically evaluate the quality of online HCC information to illuminate its current strengths and limitations. Methods: The term “hepatocellular carcinoma” was searched using Google, Dogpile, and Yippy. The overall highest-ranked 100 (“top 100”) websites were extracted based on pre-specified inclusion and exclusion criteria. A validated, evidence-based tool was used to evaluate their quality based on several benchmarks such as website affiliation, accountability, interactivity, structure & organization, readability, and content quality. Results were evaluated using descriptive and inferential statistics. Results: The search yielded over 1,100 websites. Of the top 100 websites, 53% were commercial in nature. Although 95% disclosed ownership, other measures of accountability were poor – only 30% identified their author(s), 42% cited sources, and 33% were updated within the past two years. Average readability was judged to be at a grade 11.8 level using the Flesch-Kincaid grading system, and 10.4 using the SMOG index. Both estimates were significantly higher than the traditionally recommended grade-six level ( p < 0.0001 for both). Prognosis, prevention, and incidence were the least commonly covered topics (33%, 46%, and 50% respectively). All other topics were covered with “mostly accurate” or “completely accurate” information by over 70% of websites (Table). Overall, non-commercial websites were higher in quality compared to their commercial counterparts ( p < 0.002). Conclusions: Content accuracy is generally high. However, authorship disclosure, attribution, currency, and coverage in certain topics are deficient among many HCC websites. Additionally, difficult readability may pose a barrier for patient comprehension. Healthcare professionals should be aware of the limitations of online HCC information, in order to proactively guide patients to suitable resources and advocate for improvements in patient education materials. [Table: see text]
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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 | 0.196 | 0.455 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".