Surveillance urodynamics for neurogenic lower urinary tract dysfunction: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Baseline urodynamic characterization in patients with neurogenic lower urinary tract dysfunction (NLUTD) allows detection of unsafe storage and voiding pressures and optimization of these parameters through medical or surgical intervention. Surveillance urodynamics (sUDS) studies are performed in the ambulatory setting after baseline characterization, with the goal of monitoring bladder function. How often this study should be performed and the circumstances that should prompt repeated studies are unknown. The primary objective of this review is to evaluate the evidence supporting sUDS in the setting of NLUTD as assessed by whether the study leads to 1) change in patient management; 2) determination of new findings not suggested by imaging or symptoms; and 3) demonstration of superior outcomes compared to observation. The secondary objective is to review sUDS practice patterns among urologists in their assessment of NLUTD. METHODS: PubMed, EMBASE, and Cochrane Library databases were reviewed for English-language literature published between January 1975 and March 2018. RESULTS: Twenty-eight independent articles (1368 patients, 9486 patient-years of followup) were included. Given heterogeneous data, 49% of 263 subjects were asymptomatic, yet demonstrated sUDS abnormality prompting treatment. Eight cross-sectional studies (four spinal cord injury [SCI], two NLUTD, two spina bifida) surveyed urologists regarding current sUDS patterns; 53% of 498 respondents perform sUDS between one and three years. CONCLUSIONS: Evidence supporting optimal surveillance for NLUTD is lacking. Level 2b-4 evidence suggests that sUDS is likely to modify patient treatment and often demonstrates findings that modify treatment in the absence of symptoms or imaging changes.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".