Debilitating Lower Urinary Tract Symptoms in the Post-Renal Transplant Population Can Be Predicted Pretransplantation
Bibliographic record
Abstract
BACKGROUND: Overactive bladder and benign prostatic hyperplasia commonly cause lower urinary tract symptoms (LUTS) in the renal transplant (RTx) population. These LUTS may be masked by low urine output and may pose risk to renal allografts after RTx. Our purpose was to determine the frequency and severity of LUTS in RTx patients and to determine if patients at highest risk for LUTS could be predicted by validated questionnaires or other pre-RTx characteristics. METHODS: All patients who underwent RTx between 2005 and 2010 were asked to complete the validated Overactive Bladder Questionnaire based on patient symptoms before RTx and at 6 and 12 months after RTx. Male patients also completed the International Prostate Symptom Score survey. Patient, donor, and RTx characteristics and outcome data were collected. RESULTS: Of 466 RTx recipients, 23.8% responded and 89 patients were eligible for participation in the study. LUTS were common both before and after RTx as measured by the Overactive Bladder Questionnaire and International Prostate Symptom Score. Pre-RTx survey scores were predictive of moderate to severe post-RTx LUTS (relative risk, 4.1-18.0; P≤0.05). Low urine output of less than 250 mL per day was also predictive of moderate to severe post-RTx LUTS (relative risk, 2.9-5.9; P<0.02). CONCLUSIONS: Validated questionnaires and low urine output before transplantation may be used to identify patients at highest risk for LUTS after RTx. Identification of at risk patients would allow earlier initiation of treatment strategies to improve patient quality of life.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".