YouTube Searching and Self-Treatment Behaviors Among Patients With Benign Paroxysmal Positional Vertigo Before and After Clinic Visits: Prospective Observational Study
Bibliographic record
Abstract
Background: YouTube has become a popular platform for patients seeking health-related information, including guidance on managing benign paroxysmal positional vertigo (BPPV). As self-diagnosis and self-treatment through online content grow more common, concerns have arisen regarding their influence on patients' health care decisions and treatment outcomes. However, little is known about how YouTube use and self-treatment behaviors change before and after clinical consultation, or whether these behaviors affect standard care for BPPV. Objective: This study aimed to investigate changes in patients' YouTube searching and self-treatment behaviors before and after clinic visits for BPPV and to assess whether self-treatment influences standard in-clinic management. Methods: A prospective study was conducted with patients diagnosed with BPPV who visited an otorhinolaryngology clinic in Korea from August 2024 to July 2025. On the final day of treatment, participants completed a survey, and chart reviews were performed to collect data on age, sex, canal involvement, chronic disease status, number of canalith repositioning maneuver (CRM) sessions, and pre- and postclinic YouTube searching and self-treatment. Differences in pre- and postclinic behaviors by gender were analyzed using Generalized Estimating Equations for repeated measures. The effect of self-treatment on the number of CRM sessions was assessed using negative binomial regression after confirming overdispersion. Results: Among 147 patients (71% women), preclinic YouTube searching was reported by 28 (25%) patients, while postclinic searching decreased to 21 (14%) patients. Gender-stratified Generalized Estimating Equations analysis showed women had significantly higher odds of preclinic YouTube searching compared to postclinic (odds ratio [OR] 2.389, 95% CI 1.195-4.778, P=.01). Additionally, women with chronic disease had significantly lower odds of self-treatment (OR 0.13, 95% CI 0.016-0.976, P=.047). Negative binomial regression showed no significant association between self-treatment status and the number of CRM sessions. Unlabelled: This study demonstrates that YouTube searching and self-treatment behaviors for BPPV change following clinical consultation. These findings highlight the importance of patient education during clinical encounters in addressing previsit online information use and mitigating inappropriate self-treatment practices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".