Ambulant monitoring of bladder oxygenation and hemodynamics using wireless near-infrared spectroscopy
Bibliographic record
Abstract
INTRODUCTION: Near-infrared spectroscopy (NIRS) non-invasively detects changes in the concentration of the chromophores oxygenated (ΔO2Hb) and deoxygenated hemoglobin (ΔHHb) as the bladder detrusor muscle contracts during voiding. Such data provide novel information on bladder oxygenation and hemodynamics. We evaluated the feasibility of monitoring ambulant subjects using a wireless NIRS device. METHODS: The wireless device uses paired light-emitting diodes (wavelengths 760 and 850 nm) and a silicon photodiode detector. We monitored 14 asymptomatic subjects (10 adults, 4 children) and 6 symptomatic children with non-neurogenic lower urinary tract dysfunction (NLUTD) during spontaneous voiding after natural filling. The device was taped to the abdominal skin 2 cm above the symphysis pubis across the midline. The wireless NIRS data (patterns of change in chromophore concentration) were compared between subjects and to the data obtained using a laser-powered instrument. RESULTS: Graphs of ΔO2Hb, ΔHHb and total hemoglobin (ΔtHb) were obtained from all 20 patients. Data during uroflow showed reproducible patterns of bladder chromophore change between asymptomatic subjects (rise in ΔtHb/ΔO2Hb), consistent with laser instrument data. In contrast, all 6 symptomatic children had a negative trend in ΔtHb, with falls in ΔO2Hb. One adult experienced "shy" bladder and changes in hemodynamics/oxygenation occurred while bladder volume was unchanged. CONCLUSIONS: Wireless NIRS bladder monitoring is feasible in ambulant adults and children; wireless and laser-derived data in asymptomatic subjects are comparable. Pilot data suggest that subjects with symptomatic NLUTD have impaired bladder oxygenation/hemodynamics. The fact that chromophore changes occur when bladder volume remains constant supports the concept that NIRS data are a physiologic measure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".