The Evolution of Kidney Stone Information Available to Patients: Interest Trends of Social Media and Quality Assessment of Kidney Stone Smartphone Apps
Bibliographic record
Abstract
PurposeTo identify what information kidney stone patients want and the resources they use to find it, and to evaluate kidney stone-related smartphone apps based on their actionability, understandability, quality, and reliability.MethodsGoogle Trends was used to assess searches related to kidney stones and related smartphone applications (apps) from 2019 to 2021. A questionnaire aimed at ascertaining where patients obtain kidney stone-related information was posted on popular Facebook groups and one Reddit group. Seven popular kidney stone-related apps were evaluated for reliability, quality, actionability, and understandability. Univariate statistical analysis, search volume index, and descriptive statistics were used to assess correlations and impact of variables on outcomes of interest.ResultsBetween 2019 and 2021, the peak search volume index of kidney stones was in the summer and winter. Questionnaire participants obtain most information from their doctor (45%), Reddit and Facebook groups (43%), YouTube (9%), and smartphone apps (4%). 23% reported using a kidney stone app at least once to obtain information. The average smartphone app overall has poor reliability (2.43, P <0.001) and quality (1.96, P = 0.039) and poor review of treatment options with side effects (1.36, P = 0.689), and does not encourage shared decision-making (2.57, P = 0.162). Poor actionability was found in all apps, and good understandability was found in 6 of the 7 apps.ConclusionWhile physicians are still the most-used resource for patients, patients are increasingly using online platforms and smartphone applications. Urologists should consider engaging kidney stone patients through such platforms to provide reliable educational information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".