Lower urinary tract dysfunction in uncommon neurological diseases: A report of the neurourology promotion committee of the International Continence Society
Bibliographic record
Abstract
The management of patients with neurogenic lower urinary tract dysfunction has been well-described, however this is most frequently discussed for common conditions such as spinal cord injury or multiple sclerosis. Our objective was to review uncommon neurologic disorders and summarize both the underlying disease process, and the relevant disease-specific research on the impact of the neurologic condition on the lower urinary tract. Among the degenerative and traumatic brain disorders, we have included frontotemporal dementia, amyotrophic lateral sclerosis, Huntington’s Disease, progressive supranuclear palsy, corticobasal degeneration, multiple system atrophy, and traumatic brain injury. Among the autoimmune disorders, we reviewed transverse myelitis, neuromyelitis optica spectrum disorders, Myelin oligodendrocyte glycoprotein antibody-associated disease, glial fibrillary acidic protein astrocytopathy, and meningitis-retention syndrome (a form of aseptic meningitis that presents with urinary retention). Hereditary spastic paraplegia, VACTERL association, and several peripheral neuropathies (Guillain Barre syndrome, chronic inflammatory demyelinating polyneuropathy, autoimmune autonomic gangliopathy, Wolfram syndrome spectrum disorder (a progressive peripheral neuropathy disorder with early onset diabetes, optic atrophy and megacystis in the early stage), Charcot Marie Tooth disease, and amyloid neuropathy are included. Practice points specific to the disorders are included where appropriate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".