Technological advances in NIRS devices and parameters measured: examples from clinical studies of the urologic system
Bibliographic record
Abstract
Background: Since the original application of NIRS to study the bladder, a series of advances have enabled the urologic system to be more comprehensively studied, and range of devices now provide previously unavailable physiologic measurements. As in other fields, this evolution follows progressive improvements in the technology, the availability of new components and materials, and construct of novel software and algorithms. Methods: Review of the literature describing NIRS monitoring of the urologic system (bladder, pelvic floor, urinary sphincter, brain). Results: The advent of lasers and development of fiberoptics established NIRS as a viable entity, and allowed transcutaneous monitoring of physiologic changes in the bladder detrusor muscle during spontaneous voiding. Small light emitting diodes which traded depth of penetration for portability led to wearable devices when combined with wireless capability; emitter and photodiode refinement, spatially resolved geometry, and software and algorithm development spawned the current generation of compact, robust multipotential devices, including a transvaginal interface able to quantify reoxygenation recovery in pelvic floor muscles, and interrogate the urinary sphincter. Functional NIRS allows brain mediated neural activity linked to bladder sensation and control of voiding to be mapped in real time during evaluation of voiding dysfunction; simultaneous fNIRS during fMRI is also feasible. Wearable devices linked to wireless peripherals enable serial monitoring at home of voiding parameters once only available in the hospital setting; exploration of artificial intelligence will further expand the urologic relevance of NIRS. Conclusions: The technological advances evident in urologic applications of NIRS mirror those occurring in other fields.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".