Evaluation of educational content of YouTube videos relating to neurogenic bladder and intermittent catheterization
Bibliographic record
Abstract
INTRODUCTION: Many patients conduct internet searches to manage their own health problems, to decide if they need professional help, and to corroborate information given in a clinical encounter. Good information can improve patients' understanding of their condition and their self-efficacy. Patients with spinal cord injury (SCI) featuring neurogenic bladder (NB) require knowledge and skills related to their condition and need for intermittent catheterization (IC). METHODS: Information quality was evaluated in videos accessed via YouTube relating to NB and IC using search terms "neurogenic bladder intermittent catheter" and "spinal cord injury intermittent catheter." Video content was independently rated by 3 investigators using criteria based on European Urological Association (EAU) guidelines and established clinical practice. RESULTS: In total, 71 videos met the inclusion criteria. Of these, 12 (17%) addressed IC and 50 (70%) contained information on NB. The remaining videos met inclusion criteria, but did not contain information relevant to either IC or NB. Analysis indicated poor overall quality of information, with some videos with information contradictory to EAU guidelines for IC. High-quality videos were randomly distributed by YouTube. IC videos featuring a healthcare narrator scored significantly higher than patient-narrated videos, but not higher than videos with a merchant narrator. About half of the videos contained commercial content. CONCLUSIONS: Some good-quality educational videos about NB and IC are available on YouTube, but most are poor. The videos deemed good quality were not prominently ranked by the YouTube search algorithm, consequently user access is less likely. Study limitations include the limit of 50 videos per category and the use of a de novo rating tool. Information quality in videos with healthcare narrators was not higher than in those featuring merchant narrators. Better material is required to improve patients' understanding of their condition.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.004 | 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".