Malignant websites? Analyzing the quality of prostate cancer education web resources
Bibliographic record
Abstract
INTRODUCTION: Prostate cancer patients are using more web resources to inform themselves about their cancer. However, patients may receive out-of-date or inaccurate information due to lack of regulation. The current study looks to systematically analyze the quality of websites accessed by patients with prostate cancer. METHODS: The term "prostate cancer" was searched in Google and the metasearch engines, Yippy and Dogpile, and the top 100 hits related to patient information were compiled from over 32 million hits. A standardized tool was used to examine 100 sites with respect to attribution, currency, usability, and content. RESULTS: Of the top 100 websites relating to prostate cancer information, only 27% identified an author, of which 16% had their credentials displayed. The majority of websites disclosed ownership (97%). Over half of the websites did not include the date of the last update and of those that did, only 66% were current within two years. According to the Flesch Kincaid grade level tool for readability, the majority (87%) of sites were found to be at a high school level, while 6% were at university level. Finally, content varied among websites; 90% of sites provided information on detection and workup and treatments, but only 14% of sites included information on prognosis. CONCLUSIONS: The reliability of websites presenting prostate cancer information is questionable. There were noted deficiencies in attribution, currency, and readability. While information on detection and treatment is well-covered, information related to prognosis is lacking.
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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 | 0.007 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".