Is Early Surgical Treatment for Benign Prostatic Hyperplasia Preferable to Prolonged Medical Therapy: Pros and Cons
Bibliographic record
Abstract
Background and objectives: Treatment of lower urinary tract symptoms (LUTS) related to benign prostatic hyperplasia (BPH) has shifted over the last decades, with medical therapy becoming the primary treatment modality while surgery is being reserved mostly to patients who are not responding to medical treatment or presenting with complications from BPH. Here, we aim to explore the evidence supporting or not early surgical treatment of BPH as opposed to prolonged medical therapy course. Materials and Methods: The debate was presented with a “pro and con” structure. The “pro” side supported the early surgical management of BPH. The “con” side successively refuted the “pro” side arguments. Results: The “pro” side highlighted the superior efficacy and cost-effectiveness of surgery over medical treatment for BPH, as well as the possibility of worse postoperative outcomes for delayed surgical treatment. The “con” side considered that medical therapy is efficient in well selected patients and can avoid the serious risks inherent to surgical treatment of BPH including important sexual side effects. Conclusions: Randomized clinical trials comparing the outcomes for prolonged medical therapy versus early surgical treatment could determine which approach is more beneficial in the long-term in context of the aging population. Until then, both approaches have their advantages and patients should be involve in the treatment decision.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".