Clinical NIRS of the urinary bladder – A demonstration case report
Bibliographic record
Abstract
Urinary incontinence is a common affliction among people of all ages throughout the world. There are many causes of incontinence, treatment options are determined by the cause, and current diagnostic methods require urodynamic assessment, which involves urethral and rectal catheterization, which are uncomfortable and distasteful for patients. Since clinical near infrared spectrophotometry (NIRS) is a non‐invasive, rapid means of measuring tissue oxygenation status at the bedside, we examined whether NIRS could be useful as a diagnostic tool for bladder dysfunction. An adult patient attending an incontinence clinic for routine urodynamic testing also had NIRS data collection during the standard bladder filling regimen. NIRS optodes were placed on the skin of the intact abdomen over the supra pubic region. Changes in oxy and de‐oxy hemoglobin concentration and changes in cytochrome c oxidase net redox status via NIRS were collected at 6 Hz. The magnitudes of change that occurred during NIRS data collection are on the order of 0.5 µmol/l and the moments of change correspond to the subject′s reported sensations of bladder filling and emptying, and with conventional urodynamics. These observations suggest that NIRS may be a disruptive technology with a role to play in non‐invasive evaluation of bladder dysfunction in humans.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".