Assessment of patients with lower urinary tract symptoms where an undiagnosed neurological disease is suspected: A report from an International Continence Society consensus working group
Bibliographic record
Abstract
AIM: Lower urinary tract symptoms (LUTS) are a common urological referral, which sometimes can have a neurological basis in a patient with no formally diagnosed neurological disease ("occult neurology"). Early identification and specialist input is needed to avoid bad LUTS outcomes, and to initiate suitable neurological management. METHODS: The International Continence Society established a neurological working group to consider: Which neurological conditions may include LUTS as an early feature? What diagnostic evaluations should be undertaken in the LUTS clinic? A shortlist of conditions was drawn up by expert consensus and discussed at the annual congress of the International Neurourology Society. A multidisciplinary working group then generated recommendations for identifying clinical features and management. RESULTS: The relevant conditions are multiple sclerosis, multiple system atrophy, normal pressure hydrocephalus, early dementia, Parkinsonian syndromes (including early Parkinson's Disease and Multiple System Atrophy) and spinal cord disorders (including spina bifida occulta with tethered cord, and spinal stenosis). In LUTS clinics, the need is to identify additional atypical features; new onset severe LUTS (excluding infection), unusual aspects (eg, enuresis without chronic retention) or "suspicious" symptoms (eg, numbness, weakness, speech disturbance, gait disturbance, memory loss/cognitive impairment, and autonomic symptoms). Where occult neurology is suspected, healthcare professionals need to undertake early appropriate referral; central nervous system imaging booked from LUTS clinic is not recommended. CONCLUSIONS: Occult neurology is an uncommon underlying cause of LUTS, but it is essential to intervene promptly if suspected, and to establish suitable management pathways.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".