Near-infrared spectroscopy: validation of bladder-outlet obstruction assessment using non-invasive parameters.
Bibliographic record
Abstract
INTRODUCTION: Near infrared spectroscopy (NIRS) is a non-invasive optical technique able to monitor changes in the concentration of oxygenated and deoxygenated hemoglobin in the bladder detrusor during bladder filling and emptying. OBJECTIVE: To evaluate the ability of a new NIRS instrument and algorithm to classify male patients with LUTS as obstructed or unobstructed based on comparison with classification via conventional invasive urodynamics (UDS). METHOD: Male patients with LUTS were recruited and underwent uroflow and urodynamic pressure flow studies with simultaneous transcutaneous NIRS monitoring following measurement of post residual volume (PVR) via ultrasound. Data analysis first classified each subject as obstructed or unobstructed using the standard pressure flow data and nomogram, then compared these results with a classification derived via a customized algorithm which analyzed the pattern of change of the NIRS data plus measurements of PVR and Qmax. RESULTS: Seventy subjects enrolled: 57 data sets had all required parameters [13 incomplete sets due to: communication error between NIRS and urodynamics instruments (9); data saving error (1); damaged fiber optic cables (3)]. Two complete data sets were excluded [subjects with hematuria (2)]. Thus data from 55 subjects was analyzed. The NIRS algorithm correctly identified those diagnosed as obstructed by conventional urodynamic classification in 24 of 28 subjects (sensitivity = 85.71%) and, and those diagnosed as unobstructed in 24 of 27 subjects (specificity = 88.89%). CONCLUSION: Scores derived from NIRS data plus PVR and Qmax are able to correctly identify > 85% of subjects classified as obstructed using UDS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".