YouTube Videos as a Source of Information on Colorectal Cancer: What Do Our Patients Learn?
Bibliographic record
Abstract
YouTube is the second most visited website in the world. No studies to date have characterized and evaluated YouTube videos on colorectal cancer (CRC) although these videos could influence patient decision-making, notably regarding screening and prevention. This study aims to report the characteristics and quality of these videos as patient education resources for CRC. YouTube's search engine was queried with different search phrases relating to CRC. The first two pages of each search result were analyzed. Two specialists devised a critical appraisal tool with a list of criteria to assess the videos. Quantitative YouTube parameter analyses and criteria assessment were performed. Inter-rater agreement was assessed between three raters. A total of 46 videos were eligible to be included in the study. The videos were on average 4:51 ± 3:27 min long. The videos had 10 times as many likes as dislikes. Less than half the videos discussed risk factors and protective factors. Only 41% of videos mentioned screening tests and only about a quarter discussed them. Palliative care was only mentioned in 2% of videos. A single video could obtain a perfect score on the critical appraisal tool. Length was the unique parameter associated with a high score on the criteria list. There is thus a need for more authoritative and comprehensive videos easily identifiable by the patients. Video popularity is not associated with comprehensiveness. Currently, YouTube might not be an education resource for CRC suited to every patient.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".