Spinal Cord Magnetic Resonance Imaging for Investigation of Nonneurogenic Lower Urinary Tract Dysfunction—Can the Yield be Improved?
Bibliographic record
Abstract
PURPOSE: Magnetic resonance imaging has been used to detect occult neuropathy in patients with nonneurogenic lower urinary tract dysfunction. There is substantial controversy surrounding the role of this test for lower urinary tract dysfunction. We identified factors associated with positive magnetic resonance imaging to improve patient selection. MATERIALS AND METHODS: A case-control study was done in all pediatric patients referred to our radiology department for spinal magnetic resonance imaging primarily because of lower urinary tract symptoms between 1995 and 2004. Patients with known neurological disorders or anomalies associated with neurogenic bladder (overt spinal dysraphism, imperforate anus, etc) were excluded. A total of 80 patients with a median age of 6.5 years (range 4 to 17) were identified, of whom 47 (59%) were female. Bivariate analysis was used to evaluate the association of certain variables with positive magnetic resonance imaging findings, including patient age, gender, type of urinary symptoms, fecal soiling, abnormal neuro-orthopedic examination, lumbar cutaneous findings, resistance to medical management and urodynamic findings. RESULTS: Magnetic resonance imaging revealed spinal abnormalities in 6 cases (7.5%), including intradural arachnoid cyst in 1, sacral dysgenesis in 3, syrinx/hydromyelia in 1 and tethered cord in 1. An abnormal lumbar cutaneous finding was the only variable associated with positive magnetic resonance imaging (Fisher's exact test p = 0.002). CONCLUSIONS: Spinal magnetic resonance imaging has a low impact in the management of lower urinary tract dysfunction. With proper patient selection the pretest probability of positive magnetic resonance imaging may be increased and, therefore, many unnecessary studies may be avoided. Abnormal cutaneous findings are associated with abnormal magnetic resonance imaging.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".