Evaluation of proudP
Bibliographic record
Abstract
INTRODUCTION: We sought to assess the performance of the proudP artificial intelligence (AI) algorithm, integrated into a mobile application, in estimating uroflow curves and parameters using recorded urination sounds. METHODS: A direct comparison was made between the peak flow rate (Qmax), voided volume (VV), and uroflow curves predicted by the proudP algorithm and those obtained through established validation methods. A hardware uroflow simulator replicated uroflow profiles by precisely controlling water flow rates and extracting corresponding sound data. Ten uroflow profiles, representing typical patterns observed in male subjects, were selected. Simulation experiments with proudP were conducted using a standard toilet setup. The uroflow simulator was calibrated to reproduce uroflow profiles, and validation was performed against a Flowmaster uroflowmetry device. Statistical analysis included descriptive summaries, Bland-Altman analysis, and concordance correlation coefficient (CCC) analysis. RESULTS: The proudP accurately captured various uroflow patterns generated by the simulator, with low standard deviations in Qmax predictions and biases near zero. The standard deviations of voided volume were slightly larger, primarily due to uroflow patterns with extended voiding times. The study validated the accuracy of proudP against in-office uroflowmetry, demonstrating robustness across different smartphone models. CONCLUSIONS: ProudP proved to be as accurate as in-office uroflowmetry in estimating uroflow rate across various patterns. Its convenience in home monitoring offers patients a means to observe their urination patterns accurately, while enabling healthcare professionals to gain detailed insights remotely. ProudP emerges as an essential solution for clinical practice and urological research.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".