Methodology to establish non-invasive synchronous heart rate and heart rate variability measurement during uroflowmetry
Bibliographic record
Abstract
Heart rate (HR) and heart rate variability (HRV) are established measures related to cardiovascular disease (CVD), a known risk factor correlated with lower urinary tract symptoms (LUTS) and overactive bladder (OAB). However, no standardized methodology exists to relate HR and HRV changes to uroflowmetry, the gold-standard evaluation for LUTS. Objective: to test the feasibility of methodology for synchronous, noninvasive measurement of HR and HRV, detrusor activity and uroflowmetry. Equipment: wireless continuous wave near-infrared spectroscopy (NIRS) instruments Poralite/Portamon (Artinis Medical Systems) with 3 wavelengths (785, 808, 830nm), spatially resolved configuration, and raw optical data collection at 25 to 100Hz. Protocol: Study subjects with and without LUTS provided data on voiding symptoms (validated International Consultation on Incontinence Questionnaire). Blood pressure and body mass index measurements allowed calculation of WHO cardiovascular disease risk. Subjects then wore the Portalite over the frontal cortex (HR and HRV monitoring) and the Portamon suprapubically (detrusor oxygenation monitoring). Device recording was linked synchronously to a wireless uroflow scale. Subjects were monitored for a 1-minute pre-void baseline, during voiding, and a 1-minute post-void baseline. Bladder scans confirmed bladder volume >150cc pre-baseline and measured post-void residual volume. N=40 (17 controls, 23 cases) completed the protocol. NIRS accurately monitored HR, HRV, detrusor oxygenation, and uroflowmetry in all cases. Synchronous, wireless monitoring to record the relationship between HR, HRV, detrusor activity, and uroflowmetry using non-invasive NIRS is feasible and generates reproducible results. This method will allow for larger scale studies analyzing the pathophysiology of CVD related to LUTS and OAB.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".