Outcomes after laser enucleation of the prostate with and without significant storage symptoms
Bibliographic record
Abstract
OBJECTIVE: To test for differences in recovery of lower urinary tract symptoms (LUTS) between patients with storage-positive vs -negative symptoms after laser enucleation of the prostate (LEP). PATIENTS AND METHODS: Consecutive storage-positive (severe storage symptoms, International Prostate Symptom Score [IPSS] storage subscore >8) vs storage-negative patients treated with LEP (November 2017-September 2022) within our tertiary-care database were identified. Mixed linear models tested for changes in IPSS and quality of life (QoL) at 1, 3 and 12 months after LEP. Multiple linear regression models tested for LUTS and QoL recovery risk factors at 1, 3 and 12 months. RESULTS: Of 291 study patients, 180 (62%) had storage-positive symptoms. There were no differences between storage-positive and -negative patients in mean adjusted total IPSS, IPSS-storage, IPSS-voiding and QoL at 12 months after LEP. In multiple linear regression models, storage-positive status was identified as a risk factor for higher IPSS at 1 month (β coefficient 2.98, P = 0.004) and 3 months (β coefficient 2.24, P = 0.04), as well as for more unfavourable QoL at 1 month (β coefficient 0.74, P = 0.006) and 3 months (β coefficient 0.73, P = 0.004) after LEP. Conversely, at 12 months there were no differences between storage-positive vs -negative patients. CONCLUSION: Storage-positive patients appear to experience similar long-term benefits from LEP compared to storage-negative patients. However, significant storage symptoms are associated with higher total IPSS and less favourable QoL at 1 and 3 months after LEP. These findings advocate for the consideration of LEP also in storage-positive cases with the need for thorough patient education especially in the initial post-LEP period.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".