Detection of the bladder volume from the neural afferent activities in dogs: experimental results
Bibliographic record
Abstract
OBJECTIVE: We evaluate the bladder volume and pressure through recording the bladder afferent activity in the sacral nerve roots in acute experiments of paraplegic dogs. These measurements are intended to report the status of the bladder and to adjust the stimulation parameters of an implantable electric stimulator. METHODS: The extraction of neural information for feedback in functional electrical stimulation is limited by the poor signal to noise ratio (SNR) in the sacral nerve recordings. We propose to inject a very low amplitude sinusoidal current with high SNR to the bladder through the nerve using a tripolar cuff electrode wrapped around the S2 nerve root. The application of this current (0.4 microA peak to peak, 30 Hz) allows detecting bladder afferent activity in its amplitude and the tissues impedance of the nerve. Acute experiments in dogs were performed to evaluate the proposed method. In each dog, the bladder infusion with saline was carried out at both slow and high filling rates. At the same time, the changes in the amplitude of the sinusoidal output voltage V(OUT) were recorded through the cuff nerve electrode. RESULTS: The data obtained from 26 acute experiments using eight dogs demonstrate that the amplitude of the recorded sinusoidal voltage V(OUT) varies proportionally with the bladder pressure during the bladder filling with saline solution. It also demonstrates that the bladder volume can be estimated from the increasing amplitude of the recorded V(OUT). CONCLUSION: This study shows that the increase in the V(OUT) is proportionally related to the increase in bladder pressure. The difference between the recorded V(OUT) during the bladder filling and the baseline V(OUT) can be a useful indicator of the changes in the bladder volume.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".