Ambulatory Urodynamic Findings Change Patient Outcomes
Bibliographic record
Abstract
Objectives Whilst ambulatory urodynamics (aUDS) may be used as a second-stage test for patients with refractory lower urinary tract symptoms (LUTS) having non-diagnostic conventional urodynamics (UDS), the evidence for their use is limited. We have assessed the diagnostic utility and consequent symptomatic outcome of aUDS in patients with refractory LUTS. Methods A retrospective review of a prospectively acquired urodynamics database was made of 84 consecutive patients (23 male) with a median age 50.5 years (range 18 to 79) having aUDS following non-diagnostic or contradictory baseline UDS over a 12-month period. Patient demographics and urodynamic and clinical diagnosis before and after aUDS were recorded. Forty-six patients (55%) had formal urinary symptom assessment recorded before and a minimum of 6 months following aUDS-related change in management. Results Eighty-two patients (98%) had a urodynamic diagnosis made following aUDS, 57(68%) of whom had detrusor overactivity (DO); the final 2 patients had no abnormalities detected on aUDS. Change in primary UDS diagnosis occurred in 66 patients (79%). Of these 66 patients, 59 (89%) also had their clinical diagnosis changed, and 55 (83%) had their management pathway changed. There was a significant improvement in urinary symptoms 6 months following aUDS. Conclusion Change in primary diagnosis following aUDS led to a significant change in treatment care pathway and resulted in significant improvement in urinary symptoms.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".