Polycystic Ovary Syndrome and Exercise: Evaluation of YouTube Videos
Bibliographic record
Abstract
OBJECTIVES: Polycystic ovary syndrome (PCOS) is a common endocrine disorder in the reproductive female population. These young patients often and easily watch YouTube videos on the Internet to learn about their condition and find a natural solution. Our goal is to analyze the contents of PCOS exercise videos. METHODS: In July 2022, research data were collected by typing the term "PCOS exercise" in the search tab on the incognito YouTube page. One hundred and ninety eight videos that met the inclusion criteria were analyzed in detail. The basic data of the videos available on YouTube was recorded. In addition, the DISCERN, global quality score (GQS), and video power index (VPI) scoring systems were calculated by two independent researchers. RESULTS: The profiles of the video uploaders were: health employee 28 (14.1%), nutritionist 25 (12.6%), sports trainer 48 (24.2%), patient 21 (10.6%), undefined 76 (38.4%), and their countries were: India 91 (46%), Europe and England 36 (18.2%), USA and Canada 54 (27.3%), and other countries 17 (8.6%). The distribution of video content was yoga 58 (29.3%), aerobic exercise 38 (19.2%), strengthening exercise 44 (22.2%), and unified 58 (29.3%). The mean values were: video duration (15.27±11.27), total views (3,070,957±16,474,197), likes (48,116±283,308), dislikes (930±4102), VPI (97.82±7.28), GQS (3.89±1.05), DISCERN (33.62±10.42), subscriber counts (985,614±2,222,354), and comment counts (1741±10,689). Europe-England and America-Canada videos were of better quality for DISCERN and GQS scores than those from other countries. CONCLUSION: Overcoming PCOS requires a lifestyle change, including exercise and diet. There is no consensus on which type of exercise is better yet. However, the necessity of regular exercise is known. We showed yoga and Indian hegemony in YouTube "PCOS exercise" videos.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 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".