Lower urinary tract dysfunction in uncommon neurological diseases, Part III: A report of the Neuro-urology Promotion Committee of the International Continence Society
Bibliographic record
Abstract
This is the third manuscript from the Neuro-urology Promotion Committee of the International Continence Society (ICS) discussing uncommon neuro-urological conditions that are not well described in urological literature. Readers are referred to the previous documents for a more detailed understanding of how neurological disease might affect lower urinary tract function ( https://doi.org/10.1016/j.cont.2022.100022) ( https://doi.org/10.1016/j.cont.2023.101043). Eleven conditions are covered. This includes five genetic conditions — Angelman disease, spinocerebellar ataxias, Fabry's disease, neurofibromatosis and spinal muscular atrophy. Two infection associated conditions are covered including Human T-lymphotropic Virus Type 1 associated myelopathy/ tropical spastic paraparesis and Creutzfeldt–Jackob disease. Four other conditions covered are cerebral palsy, Lennox–Gastaut syndrome, idiopathic normal pressure hydrocephalus and spinal vascular malformation. Key aspects and specific recommendations related to clinical practice are summarized in tables. Knowledge of unusual neurological conditions that can affect the lower urinary tract is important for ensuring a timely and precise diagnosis. This document from the ICS, along with Parts I and II published earlier, can serve as a reference for clinicians presented with unusual forms of neurogenic lower urinary tract dysfunction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".