Variable Quality and Readability of Patient-oriented Websites on Colorectal Cancer Screening
Bibliographic record
Abstract
Background & AimsThe efficacy of colorectal cancer (CRC) screening is dependent on participation and subsequent adherence to surveillance. The internet increasingly is used for health information and is important to support decision making. We evaluated the accuracy, quality, and readability of online information on CRC screening and surveillance.MethodsA Website Accuracy Score and Polyp Score were developed, which awarded points for various aspects of CRC screening and surveillance. Websites also were evaluated using validated internet quality instruments (Global Quality Score, LIDA, and DISCERN), and reading scores. Two raters independently assessed the top 30 websites appearing on Google.com. Portals, duplicates, and news articles were excluded.ResultsTwenty websites were included. The mean website accuracy score was 26 of 44 (range, 9–41). Websites with the highest scores were www.cancer.org, www.bowelcanceraustralia.org, and www.uptodate.com. The median polyp score was 3 of 10. The median global quality score was 3 of 5 (range, 2–5). The median overall LIDA score was 74% and the median DISCERN score was 45, both indicating moderate quality. The mean Flesch–Kincaid grade level was 11th grade, rating the websites as difficult to read, 30% had a reading level acceptable for the general public (Flesch Reading Ease > 60). There was no correlation between the Google rank and the website accuracy score (rs = -0.31; P = .18).ConclusionsThere is marked variation in quality and readability of websites on CRC screening. Most websites do not address polyp surveillance. The poor correlation between quality and Google ranking suggests that screenees will miss out on high-quality websites using standard search strategies. The efficacy of colorectal cancer (CRC) screening is dependent on participation and subsequent adherence to surveillance. The internet increasingly is used for health information and is important to support decision making. We evaluated the accuracy, quality, and readability of online information on CRC screening and surveillance. A Website Accuracy Score and Polyp Score were developed, which awarded points for various aspects of CRC screening and surveillance. Websites also were evaluated using validated internet quality instruments (Global Quality Score, LIDA, and DISCERN), and reading scores. Two raters independently assessed the top 30 websites appearing on Google.com. Portals, duplicates, and news articles were excluded. Twenty websites were included. The mean website accuracy score was 26 of 44 (range, 9–41). Websites with the highest scores were www.cancer.org, www.bowelcanceraustralia.org, and www.uptodate.com. The median polyp score was 3 of 10. The median global quality score was 3 of 5 (range, 2–5). The median overall LIDA score was 74% and the median DISCERN score was 45, both indicating moderate quality. The mean Flesch–Kincaid grade level was 11th grade, rating the websites as difficult to read, 30% had a reading level acceptable for the general public (Flesch Reading Ease > 60). There was no correlation between the Google rank and the website accuracy score (rs = -0.31; P = .18). There is marked variation in quality and readability of websites on CRC screening. Most websites do not address polyp surveillance. The poor correlation between quality and Google ranking suggests that screenees will miss out on high-quality websites using standard search strategies.
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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 | 0.004 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".