The quality of YouTube videos on radiotherapy and prostatectomy for prostate cancer
Bibliographic record
Abstract
INTRODUCTION: Prostate cancer ranks as the third leading cause of death among Canadian men and is primarily treated with radiotherapy and prostatectomy. Given YouTube's significant global traffic, patients often turn to it for information on treatment and side effects. This study assessed YouTube videos for prostate cancer patients, focusing on radiotherapy, prostatectomy, and side effect management. METHODS: The study analyzed 50 YouTube videos, comparing their accuracy and coverage against the National Comprehensive Cancer Network (NCCN), UpToDate, and cancer.ca. Two raters were involved in the review of the videos to ensure inter-rater reliability. RESULTS: Video lengths ranged from 1-60 minutes (mean 11 minutes) and creation dates ranged from 2012-2021. Videos were presented by physicians, patients, or allied health professionals (75%, 16%, and 8%, respectively). Results showed physician presenters had a Video Power Index (video popularity) of 23.45, while patient presenters had an average of 61.36 (almost three times as popular as physician-led videos). The overall accuracy of videos showed that 82% demonstrated completely accurate and detailed information, 18% showed partially complete information, and 76% showed no biased information. No false information was found in any videos. CONCLUSIONS: This study showed that while the YouTube informational videos included good coverage of treatment side effects, there were gaps in information regarding quality of life after treatment or management of side effects. Information from this study can benefit the provider-patient interaction, with the hope that healthcare providers create more videos on quality of life after treatment and management of side effects to satisfy patient needs.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".