How to Prevent Kidney Stones: What Is Online Video Content Imparting to Our Patients?
Bibliographic record
Abstract
BackgroundUreteric calculi are amongst the most painful conditions encountered in medicine, with some patients describing their experience as worse than childbirth [1].Because of this threat of pain, patients will seek methods to prevent nephrolithiasis formation and will often refer to unverified sources for this information.As clinicians, we must provide evidence-based patient education and recognise that patients may have misinformed preconceptions when seeking medical treatment.In recent years, particularly due to the COVID-19 global pandemic, the internet has become a readily accessible source of health information [2].Following Google, YouTube is the second-most visited website worldwide, with the largest collection of free-to-access video content and an ever-growing influence with health information distribution[3].This is of concern, as unregulated videos uploaded to this platform may provide misleading information and perpetuate a misunderstanding of the appropriate treatment for potentially life-threatening health conditions. AimsThe aim of this study was to assess the accuracy, understandability and actionability of online video content available to patients regarding how to prevent kidney stones.How to Prevent Kidney Stones: What Is Online Video Content Imparting to Our Patients?
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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 | 0.006 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".