Multiple sclerosis and lower urinary tract symptoms: A survey of prevalence, characteristic and urological evaluations
Bibliographic record
Abstract
Objective: Most multiple sclerosis patients have urological complications such as lower urinary tract symptoms. This study was conducted to evaluate the prevalence of these symptoms and whether they result in a urological evaluation. Methods: A cross-sectional study of 517 multiple sclerosis patients at Tehran’s referral multiple sclerosis center and neurology clinics between 2018 and 2022 was performed. Data were collected through interviews after patients completed informed consent forms. Urological examinations, including urine analysis and ultrasonography, were evaluated as final assessments. The data were analyzed using descriptive and inferential statistical tests in Statistical Package for Social Science. Results: Among all participants, the prevalence of lower urinary tract symptoms was 73% ( n = 384), with urgency (44.8% n = 232) being the most common symptom. The prevalence of intermittency was significantly higher among women ( p = 0.004). There was no gender-significant difference in terms of the prevalence of other symptoms ( p > 0.050). Lower urinary tract symptoms were significantly correlated with age, clinical course, disease duration, and disability ( p < 0.001). Additionally, 37.3% and 18.7% of patients with lower urinary tract symptoms, as well as 17.9% and 37.5% of patients with multiple sclerosis attacks, respectively, had undergone urine analysis and ultrasonography. Conclusion: Multiple sclerosis patients rarely undergo urological evaluations during the course of their disease. Proper assessment is essential as these symptoms are among the most detrimental manifestations of this disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".