Quality Analysis of Online Patient Resources for Hepatocellular Carcinoma
Bibliographic record
Abstract
GOAL: This study aims to evaluate the quality of online hepatocellular carcinoma (primary liver cancer) resources by using a validated tool to determine the strengths and limitations of hepatocellular carcinoma Web sites designed for patient education. BACKGROUND: In recent years, online health information-seeking behavior has become more prevalent. Meanwhile, hepatocellular carcinoma incidence rates have also increased. However, there is currently limited literature assessing the quality of online hepatocellular carcinoma information. MATERIALS AND METHODS: The term "hepatocellular carcinoma" was searched using the search engine Google and the meta-search engines Dogpile and Yippy. A validated rating tool was used to assign quality scores to 100 Web sites based on the domains of Web site affiliation, accountability, interactivity, structure and organization, readability, and content quality. Overall quality scores were tallied for all Web sites. RESULTS: Noncommercial hepatocellular carcinoma Web sites received significantly higher overall quality scores compared with their commercial counterparts. Overall, 30% of the Web sites identified their author(s), 42% cited sources, and 33% were updated within the past 2 years. The majority of Web sites utilized at least 1 interactive feature and 4 structural tools. Average readability was at a grade 11.8 level using the Flesch-Kincaid grading system, which is significantly higher than the recommended grade 6 level. Definition and treatment were the most commonly covered topics, while prevention and prognosis were the least commonly covered. CONCLUSIONS: The quality of online hepatocellular carcinoma information is highly variable. Health care professionals should be aware of its limitations and be proactive in guiding patients to reliable resources.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".