Bibliographic record
Abstract
Introduction: Patients electing benign prostatic hyperplasia (BPH) surgery may wish to cease BPH medical therapy, though some patients may continue or begin medication post-surgery to treat residual symptoms.In this large-scale healthcare claims analysis, we produce rates of continued and de novo BPH medical therapy following transurethral resection of the prostate (TURP), GreenLight photoselective vaporization (PVP), and UroLift prostatic urethral life (PUL) procedures.Methods: Patients with ≥1 year of followup who underwent outpatient TURP (n=20 319), GreenLight (n=10 517), or PUL (n=5793) were identified within this representative sample of 2015-2021 Medicare and commercial claims.Linking pharmaceutical claims to outpatient surgical claims produced rates of continuous and de novo use of alpha-blockers, 5ARI, or combination therapy; only patients with ≥2 instances of medical therapy prescriptions following BPH surgery were included.Perioperative medication usage was defined as ≥2 medication prescriptions within three months only and not beyond; prolonged use was assessed through one and five years postoperative.Results: Rates of perioperative medical therapy were similar between all treatments.The rates of continued medication use following PUL, TURP, and PVP were 2.5%, 4.0% and 4.2%, respectively.De novo use was low after all therapies; lowest for PUL (0.5%) and similar between TURP (0.9%) and PVP (1.0%).The total one-year medical therapy rate was lowest for PUL (3.9%, TURP 6.1%, PVP 6.5%).Rates of continued and de novo use were: PUL (8.4% continued, 1.0% de novo), TURP (7.0% continued, 2.0% de novo), and PVP (7.2% continued, 1.7% de novo).The total combined five-year medical therapy rate was similar between all three therapies: PUL (10.3%),TURP (10.2%), and PVP (10.2%).Alpha-blockers were the leading BPH drug class utilized through one and five years post-PUL, TURP, and PVP.Conclusions: Post-surgery medication use is an important, yet relatively unexplored, element of the BPH patient journey.Rates of medication use through one year were higher following TURP and PVP compared to PUL, and were equivalent at five years.This may indicate that in a real-world setting, TURP and PVP patients could have more advanced disease that doesn't fully respond to the benefits of the selected intervention.The five-year real-world rate of medication usage for PUL in this analysis is similar to the rate demonstrated in the LIFT pivotal trial (10.3% vs. 10.7%LIFT).
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.240 | 0.118 |
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".