First-Year Experience of Managing Urology Patients With Home Uroflowmetry: Descriptive Retrospective Analysis
Bibliographic record
Abstract
BACKGROUND: Lower urinary tract symptoms affect a large number of people of all ages and sexes. The clinical assessment typically involves a bladder diary and uroflowmetry test. Conventional paper-based diaries are affected by low patient compliance, whereas in-clinic uroflowmetry measurement face challenges such as patient stress and inconvenience factors. Home uroflowmetry and automated bladder diaries are believed to overcome these limitations. OBJECTIVE: In this study, we present our first-year experience of managing urological patients using Minze homeflow, which combines home uroflowmetry and automated bladder diaries. Our objective was 2-fold: first, to provide a description of the reasons for using homeflow and second, to compare the data obtained from homeflow with the data obtained from in-clinic uroflowmetry (hospiflow). METHODS: A descriptive retrospective analysis was conducted using Minze homeflow between July 2019 and July 2020 at a tertiary university hospital. The device comprises a Bluetooth-connected gravimetric uroflowmeter, a patient smartphone app, and a cloud-based clinician portal. Descriptive statistics, Bland-Altman plots, the McNemar test, and the Wilcoxon signed rank test were used for data analysis. RESULTS: The device was offered to 166 patients, including 91 pediatric and 75 adult patients. In total, 3214 homeflows and 129 hospiflows were recorded. Homeflow proved valuable for diagnosis, particularly in cases where hospiflow was unreliable or unsuccessful, especially in young children. It confirmed or excluded abnormal hospiflow results and provided comprehensive data with multiple measurements taken at various bladder volumes, urge levels, and times of the day. As a result, we found that approximately one-fourth of the patients with abnormal flow curves in the clinic had normal bell-shaped flow curves at home. Furthermore, homeflow offers the advantage of providing an individual's plot of maximum flow rate (Q-max) versus voided volume as well as an average or median result. Our findings revealed that a considerable percentage of patients (22/76, 29% for pediatric patients and 24/50, 48% for adult patients) had a Q-max measurement from hospiflow falling outside the range of homeflow measurements. This discrepancy may be attributed to the unnatural nature of the hospiflow test, resulting in nonrepresentative uroflow curves and an underestimation of Q-max, as confirmed by the Bland-Altman plot analysis. The mean difference for Q-max was -3.1 mL/s (with an upper limit of agreement of 13 mL/s and a lower limit of agreement of -19.2 mL/s), which was statistically significant (Wilcoxon signed rank test: V=2019.5; P<.001). Given its enhanced reliability, homeflow serves as a valuable tool not only for diagnosis but also for follow-up, allowing for the evaluation of treatment effectiveness and home monitoring of postoperative and recurrent interventions. CONCLUSIONS: Our first-year experience with Minze homeflow demonstrated its feasibility and usefulness in the diagnosis and follow-up of various patient categories. Homeflow provided more reliable and comprehensive voiding data compared with hospiflow.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| 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".