Monitoring physiologic change in the bladder in health and disease. A new biomedical application of near-infrared spectroscopy
Bibliographic record
Abstract
Biomedical applications of near-infrared spectroscopy (NIRS) utilize non-invasive optical technology to monitor alterations in tissue oxygenation and hemodynamics in real time via changes in concentration of the chromophores oxygenated and deoxygenated hemoglobin. Applications of NIRS to the bladder are novel and recent. Transcutaneous monitoring over the bladder as it fills and empties provides unique physiologic information, as hemodynamic variations in the organ's microcirculation and alterations in oxygen supply, demand and consumption in the detrusor muscle can be inferred. Such information, which is not available by other means, enhances investigation of patients with bladder dysfunction, offers new insights into causal pathology and hence potentially impact choice of pharmaceutical agents. Problem voiding is common, but the principal diagnostic test is invasive, and gives limited diagnostic and therapeutic information (pressure/flow). Continuous wave NIRS instruments are used for bladder monitoring. Initially NIR light was laser-generated; now miniaturized self-contained devices using light emitting diodes, spatial configuration of emitters to detector, and wireless capacity enhance research scope and clinical monitoring potential. NIRS bladder chromophore patterns during filling and voiding differ in health and disease. In addition to adding new knowledge re voiding dysfunction causation, data patterns characteristic of specific pathology have allowed construct of algorithms with comparable discriminant ability to current invasive diagnostic measures. Such data are physiologic because NIRS only detects change during events in the voiding cycle, pathologic changes mirror NIRS-derived effects of physiologic events (hypoxia/ischemia/fatigue) observed in other tissues (muscle/brain/spinal cord), and animal data and independent research corroborate the principal findings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".