A novel magnetic resonance imaging protocol to investigate how visual triggers impact urgency urinary incontinence
Bibliographic record
Abstract
Background: Visual stimuli are recognized to stimulate urinary urgency and urgency urinary incontinence (UUI). Current pathophysiology recognizes the importance of cortical control over micturition, but as clinicians lack any methodology to evaluate causal triggers, a MRI protocol for urologic use that explores the brain’s response to visual triggers in subjects with clinical symptoms of trigger-related UUI was developed. Methods: Using a 3 Tesla Philips Elition Scanner, structural T1 weighted images were acquired and used to define ~200 brain regions based on a validated brain atlas. Diffusion Tensor Imaging (DTI) and Myelin Water Fraction (MWF) scans were then obtained to investigate for myelin abnormalities. A functional MRI (fMRI) component followed, where, during scanning, patients were shown a defined random sequence of visual stimuli that consisted of subject-specific trigger images supplied by each subject, interspersed with neutral images. The fMRI study was performed after natural bladder filling. Results: N=10 subjects participated (6 asymptomatic controls and 4 with UUI). Debriefing confirmed that images within the sequence had triggered symptoms of UUI; tractography demonstrated robust structural connectivity between the anterior cingulate cortex and periaqueductal grey matter. Discussion: Conventional investigation of UUI lacks evaluative methodology for the impact of visual triggers on sensation of urgency and onset of incontinence, yet cortical control is recognized to be a major component of UUI in large numbers of the affected population. Conclusion: We describe the feasibility of a novel 1 hour 6 minute fMRI protocol for evaluation and quantification of the cortical mechanisms underlying visual triggers for UUI.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".