Urological applications of near infrared spectroscopy.
Bibliographic record
Abstract
OBJECTIVE: Near infrared spectroscopy (NIRS) uses light to monitor changes in the concentration of oxyhemoglobin and deoxyhemoglobin in living tissue non-invasively and in real time. Applications of NIRS in urology research and the strengths and limitations of this technology are reviewed. MATERIAL AND METHODS: A Medline and Pub-Med search using "spectroscopy" with heading terms: near infrared (NIR), near infrared spectroscopy (NIRS), urology, kidney, renal, urinary tract, bladder, prostate, testis and penis. RESULTS: Research incorporating NIRS has investigated a range of urologic conditions where a hemodynamic or vascular etiology is thought to be the underlying pathophysiology: as an aid to diagnosis in cryptorchidism, testicular torsion and vasculogenic erectile dysfunction; to evaluate renal metabolism and bladder dysfunction, and to study skeletal muscle metabolism in end stage renal disease. Strengths and limitations of NIRS relate primarily to the basic physics of how light in the NIR spectrum penetrates tissue and is scattered and absorbed. CONCLUSIONS: NIRS is a non-invasive, portable, real time measure of changes in tissue perfusion and oxygenation. In urology NIRS appears particularly applicable in ischemic conditions, and the evaluation of disorders associated with alterations in regional tissue hemodynamics (local changes in pressure, muscle contraction and urinary tract obstruction). Because the bladder detrusor can be interrogated transcutaneously NIRS may also provide a non-invasive means of evaluating patients with voiding dysfunction. Studies to date warrant further research and specific refinement of instrumentation and algorithm software for urologic applications, as NIRS could provide urologists with new methods of non-invasive physiologic diagnostic evaluation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".