Validation of transcutaneous NIRS monitoring of bladder hemodynamics and oxygenation using a rabbit model
Bibliographic record
Abstract
Transcutaneous near infrared spectroscopy (NIRS) is a recognized means of non-invasively monitoring changes in the concentration of oxygenated and deoxygenated hemoglobin in tissue. However in applications of this technique to the bladder, because the combined thickness of the detrusor muscle and the wall of the organ is only a few millimeters, the question arises whether the trends and variations in hemoglobin concentration detected transcutaneously reflect physiologic changes occurring in the detrusor or are influenced by the effect of overlying tissue on the NIRS signal. In this study a rabbit model was used so that NIRS data could be collected transcutaneously and then with the optical probe applied directly the anterior bladder wall after surgical exposure of the organ, and the data compared. Studies were done with an Oxymon dual channel spectrometer on 6 anaesthetized New Zealand white rabbits using interoptode distances adjusted for the two measurement sites, a consistent bladder filling and emptying protocol, and exposure to a brief period of controlled hypoxia (oxygen saturation decrease to 80%). Consistent data were obtained from transcutaneous and direct bladder wall measurements which confirms that transcutaneous NIRS monitoring does reflect changes occurring within the detrusor muscle in the anterior bladder wall. Hence, transcutaneous monitoring in humans using appropriate methodology and inter-optode spacing can be expected to avoid any potentially confounding signals from tissues in the abdominal wall overlying the bladder.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".