The use of mobile applications in urology. A systematic review
Bibliographic record
Abstract
Abstract Can the use of mobile applications (apps) improve quality of life and disease management in adult patients with urologic disease? Technology has created new opportunities to promote behavioural change in the daily lives of patients. One third of adults in the United States who own smartphones or tablets use health apps to improve their health. The aim of this review was to analyse whether use of mobile apps improves the quality of life and symptom management of urological patients. Databases: PubMed, CINAHL, Scopus. Search terms (free terms, MeSH): mobile apps, urologic diseases. Papers searched included all randomized controlled trials, quasi‐experimental studies, analytical cross‐sectional studies, cohort studies, and case–control studies. Nine articles were analysed. The review showed that mobile apps purpose‐built for urological patients can improve quality of life, signs and symptoms. It is necessary that nurses and physicians be familiar with these apps in order to identify and benefit those patients who deal with disabling diseases that prevent them performing their daily activities to the desired degree. Mhealth makes inroads into the medical profession, both doctors and nurses should maintain awareness not only of drug and other therapies but also of the efficacy of the mobile health apps available. Digital health treatment has arrived and can no longer be considered merely peripheral.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".