Validation of A Wireless Telemetric Bladder Pressure Monitoring System in Traumatic Thoracic Spinal Cord Injury in Yucatan Minipigs
Bibliographic record
Abstract
Neurogenic lower urinary tract dysfunction (NLUTD) is a major cause of morbidity and reduced quality of life after spinal cord injury (SCI). In pre-clinical research, small and large animal models such as rats, dogs, and minipigs have been used to investigate NLUTD through urodynamic studies (UDS) such as conventional filling cystometry. Although filling cystometry is currently considered the gold standard for bladder monitoring in pre-clinical research, this approach has several well-recognized limitations. The aim of this study was to develop and evaluate the feasibility of an implantable, radiotelemetric system for monitoring bladder pressure in a Yucatan minipig model of SCI. The transmitter was surgically implanted in the dome of the bladder and several UDS experiments were conducted to evaluate the system's effectiveness at measuring pressure compared to conventional UDS equipment. We observed a strong correlation and agreement between the transmural telemetry sensor and the UDS system. There was no significant difference between bladder compliance and baseline bladder pressure between the two sensor systems. However, the telemetry system recorded significantly lower voiding and non-voiding contraction pressure amplitudes as well as lower voiding threshold pressures and detrusor after-contraction measured with the telemetry system. The telemetry system appeared to be a reliable and accurate method for assessing bladder pressure and allowed for an evaluation of urodynamics in a pig model of SCI for several months. The application of this method could enable a more detailed in vivo evaluation of NLUTD after SCI and a better understanding of micturition behavior during natural-filling, ambulatory urodynamics.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".