Characterization of Lower Urinary Tract Dysfunction after Thoracic Spinal Cord Injury in Yucatan Minipigs
Bibliographic record
Abstract
There is an increasing need to develop approaches that will not only improve the clinical management of neurogenic lower urinary tract dysfunction (NLUTD) after spinal cord injury (SCI), but also advance therapeutic interventions aimed at recovering bladder function. Although pre-clinical research frequently employs rodent SCI models, large animals such as the pig may play an important translational role in facilitating the development of devices or treatments. Therefore, the objective of this study was to develop a urodynamics protocol to characterize NLUTD in a porcine model of SCI. An iterative process to develop the protocol to perform urodynamics in female Yucatan minipigs began with a group of spinally intact, anesthetized pigs. Subsequently, urodynamic studies were performed in a group of awake, lightly restrained pigs, before and after a contusion-compression SCI at the T2 or T9-T11 spinal cord level. Bladder tissue was obtained for histological analysis at the end of the study. All anesthetized pigs had bladders that were acontractile, which resulted in overflow incontinence once capacity was reached. Uninjured, conscious pigs demonstrated appropriate relaxation and contraction of the external urethral sphincter during the voiding phase. SCI pigs demonstrated neurogenic detrusor overactivity and a significantly elevated post-void residual volume. Relative to the control, SCI bladders were heavier and thicker. The developed urodynamics protocol allows for repetitive evaluation of lower urinary tract function in pigs at different time points post-SCI. This technique manifests the potential for using the pig as an intermediary, large animal model for translational studies in NLUTD.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".