A Quality Assessment of Information Available on Renal Cancer on YouTube
Bibliographic record
Abstract
Objectives Many people are turning to alternatives to the conventional doctor-patient relationship, such as webbased search engines and video forums for their health care information. We undertook this study to investigate the quality of videos and information on renal cancer available on the streaming platform YouTube. Methods We completed a search of YouTube (www.YouTube.com) in September 2021 with the term “kidney cancer.” The first 120 videos found which met the inclusion criteria (English speaking, duration greater than one minute, greater than 500 views, renal cancer addressed) were selected. We recorded information including duration, view count, likes, dislikes, comments, publisher, and author. The modified DISCERN tool and Global Quality Score (GQS) questionnaire were used to assess the quality of the included videos. The level of misinformation was assessed using a Likert 5-point scale. Descriptive statistics were used to analyse the collected data. A 2-sample t test was used to further analyse the quality assessment tool results before, during, and after 2016. Results Most videos were published during or after 2016 (63.3%), were predominantly created in North America (77.5%), and were presented by health care professionals (60%). The median length of the videos was 4.23 (1.01 to 65.55) minutes, and the median number of views was 3087 (514 to 228 152). The median number of likes and dislikes was 24 and 5, respectively. The median modified DISCERN score was 3, the median GQS score was 3, and the grading for overall level of misinformation was moderate. Conclusion The quality of information accessed from YouTube on kidney cancer is of a low to moderate overall standard with significant levels of misinformation. YouTube should not be used alone for educational purposes on renal cancer by patients or the public. It is best used in conjunction with information and advice from a medical practitioner and the health care system.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.009 | 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".