The relationship between bladder wall thickness and lower urinary tract symptoms: Does bladder wall thickness change after alpha-blocker therapy with alfuzosin?
Bibliographic record
Abstract
INTRODUCTION: We evaluate the association between lower urinary tract symptoms (LUTS) and bladder wall thickness (BWT) and investigate whether alfuzosin might improve BWT. METHODS: We retrospectively reviewed the data of 164 patients with LUTS. Patients were divided into 2 groups according to BWT (Group 1: BWT ≤5 mm, n = 69; Group 2: BWT >5 mm, n = 95). Age, international prostate symptom score (IPSS), maximum and average urinary flow rates (Qmax and Qave), quality of life (QoL), postvoid residual (PVR) urine volume, prostate volume and prostate-specific antigen (PSA) were compared between the 2 groups. In total, 102 patients underwent transurethral resection of the prostate (TURP) and 62 patients were treated with alfuzosin. We compared BWT, Qmax, Qave, IPSS, QoL, PVR and PSA before and at the sixth month of alfuzosin therapy. A p value of <0.05 was considered statistically significant. RESULTS: The mean BWT of Group 1 was 3.72 ± 0.56 mm and Group 2 was 6.43 ± 1.13 mm. There was a significant difference between the 2 groups in terms of mean Qmax and PVR. There was no statistical difference between the groups in terms of Qave, IPSS, QoL, prostate volume and PSA. There was significant difference between BWT before (6.8 ± 2.1) and after (4.6 ± 1.3) treatment with alfuzosin in 62 patients (p = 0.02). There was a significant difference between pre- and post-treatment values of mean Qmax, Qave, IPSS, QoL score, and PVR with alfuzosin. CONCLUSION: BWT is a non-invasive and effective test to evaluate patients with lower urinary tract obstruction and may be used for showing the effectiveness of alpha-blocker therapy in patients with LUTS.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".