Bibliographic record
Abstract
The quest to find the perfect treatment for male LUTS has consumed urologists for decades. On the medical front, the battle ground has been α-blockers with varying receptor selectivity and tolerability, 5-α-reductase inhibitors which inhibit different isoforms, anticholinergics (which transitioned from contraindication to cutting edge) and, more recently, β3 agonists and phosphodiesterase-5 (PDE5) inhibitors. Similarly, the surgical management of BPH (which may or may not be the cause of male LUTS) has seen numerous procedures and operations investigated and marketed with promises to cut, burn, cook, freeze, enucleate or ablate with a laser, pressure wash, steam, cut off the blood flow, staple open, stent open or surgically remove adenoma, all with the goal of reducing urinary symptoms. Nagasubramanian et al. [1] investigated whether the addition of tadalafil 5 mg to tamsulosin 0.4 mg offers a benefit over tamsulosin 0.4 mg alone. This randomized double-blind placebo-controlled study was carried out in men who were aged 60 years, on average, with moderate symptom scores (IPSS 15–16/35), and who were mostly dissatisfied with urinary quality of life; flow rates were low but reasonably maintained (~ 10 mL/s), and most of the men had post-void residual urine volumes < 100 mL. The addition of daily tadalafil resulted in a 1.7-point improvement in the IPSS, a 0.7-point improvement in urinary quality of life, and an almost 2-mL/s improvement in peak flow (and not surprisingly an improvement in erectile function). The improvement in flow rate is notable as an objective measure of effect, and not something that has been shown consistently with PDE5 inhibitors. A recent systematic review found only three out of nine randomized trials showed a significant improvement in flow rate when PDE5 inhibitors plus α-blockers were compared with α-blockers alone [2]. There are now numerous combinations of medical therapy that can be offered to men with LUTS, and it is useful to look to some of the recent meta-analyses to understand the magnitude of effect these medications may offer, and then consider whether the additional medication is worth the added expense, possible medication interactions and potential side effects. Figure 1 summarizes the changes in the IPSSs from various meta-analyses which studied different combinations of α-blockers and PDE5 inhibitors [2-5], with fairly consistent results: either an α-blocker or a PDE5 inhibitor helps improve LUTS to a similar degree, and the addition of one to the other improves LUTS a little bit more. The challenge is that these improvements are modest, and a smaller proportion of patients are actually ‘responders’ (those with an improvement above the minimally clinically important threshold of the IPSS and perceptible to the patient). With all the combinations of medical therapy, it is becoming increasingly difficult to keep track of the various possibilities. A nice network meta-analysis [6] (although now 6 years old) addressed the question about the various permutations of medical therapy, and their conclusion was that an α-blocker plus a PDE5 inhibitor was best for improving LUTS, which mirrors the conclusion of the present study [1]. A better understanding of male LUTS clusters [7] now needs to be integrated with study of the various medical and surgical treatment options to more accurately tailor specific treatment to the right patient. None declared.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".