A fountain of knowledge? The quality of online resources for testicular cancer patients
Bibliographic record
Abstract
INTRODUCTION: Testicular cancer is the most common solid malignancy diagnosed in young men aged 15-29. This population is also the age group that searches most actively for health information online. This study systematically evaluates the quality of websites available to patients with testicular cancer. METHODS: The term "testicular cancer" was inputted into the search engines Google, Dogpile, and Yippy. The top 100 websites intended for patient education were compiled. A validated structural rating tool was used to evaluate the websites with respect to attribution, currency, disclosure, interactivity, readability, and content. RESULTS: Less than half of the websites (44) disclosed authorship. Sixty-one websites provided a last modified date, and of those, 46 were updated in the last two years. The average readability level was 11.01 using the Flesh-Kincaid grade level system. The most accurate topic was treatment, with 82 websites being completely accurate and containing all required information. The least accurate topic was prognosis, with 27 being completely accurate. CONCLUSIONS: These results show that authorship and currency are lacking in many online testicular resources, making it difficult for patients to validate the reliability of information. The high average readability of testicular cancer websites can affect comprehension. Topics such as prognosis were incompletely covered although represent an area for which patients often seek more information. These results can be used to counsel patients on the strength and weaknesses of online testicular cancer resources.
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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 | 0.002 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".