Validation of a real‐time bladder sensation meter during oral hydration in healthy adults: Repeatability and effects of fill rate and ultrasound probe pressure
Bibliographic record
Abstract
OBJECTIVES: A non-invasive protocol was previously developed using three-dimensional ultrasound and a sensation meter to characterize real-time bladder sensation. This study the protocol by measuring the effects of fill rateand ultrasound probe pressure during oral hydration. METHODS: Healthy volunteers with no urinary symptoms (based on International Consultation on Incontinence Questionnaire on Overactive Bladder surveys) were recruited into an oral hydration study. Throughout two complete fill-void cycles, participants drank 2 L Gatorade G2 (The Gatorade Company, Inc., Chicago, Illinois) and used a touch-screen sensation meter to record real-time bladder sensation (0%-100%). The study was repeated three times, once per week (Visits A, B, and C). In Visits A and B, ultrasound was used to measure bladder volume every 5 minutes. Ultrasound was not used in Visit C except at 100% capacity. Volume data from Visit B were used to estimate volumes throughout the fills in Visit C. Sensation-capacity curves were generated for each fill for comparative analysis. RESULTS: Ten participants completed three visits (60 total fills). Increased fill rate led to decreased sensation throughout filling, andultrasound probe pressure led to increased sensation. Participants reported higher sensation at low volumes during Fill 1 of Visit A before training with the sensation meter. Sensation curves with intermittent ultrasound showed repeatability for Fill 2 in Visits A and B. Fill rate and ultrasound probe pressure affect real-time bladder sensation during oral hydration. CONCLUSIONS: This study demonstrated repeatability of real-time bladder sensation during a two-fill oral hydration protocol with ultrasound.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".