Search trends in the treatment for benign prostatic hyperplasia: A twenty-year analysis
Bibliographic record
Abstract
Objective: Minimally invasive treatments for benign prostatic hyperplasia (BPH) have seen an increase in usage in recent years. We aimed to determine what types of events may influence patient search habits related to surgical BPH treatments. Methods: Google Trends was used to determine the frequency of searches for different minimally invasive and prostatic ablative treatments for BPH in the United States. The procedures including transurethral resection of the prostate (TURP), Aquablation therapy (Aquablation), Greenlight laser therapy (Greenlight), transurethral needle ablation, transurethral microwave thermotherapy, Urolift (prostatic urethral lift [PUL]), Rezum, iTind, holmium laser enucleation of the prostate, simple prostatectomy, and prostatic artery embolization were compared. Results: From January 1, 2004 to February 28, 2023, the number of internet search queries have increased for TURP, PUL, Rezum, prostatic artery embolization, and holmium laser enucleation of the prostate. There has been a slight decrease in searches for Greenlight, transurethral needle ablation, transurethral microwave thermotherapy, iTind, simple prostatectomy, and Aquablation. Conclusion: Despite increased searches of alternatives, TURP remains the most searched BPH procedure. Additionally, search habits may be influenced by several factors including government approval, corporate acquisition, and marketing campaigns. It is important for physicians to understand the types of events that may cause patients to inquire about certain treatments for better quality health information and clinical visits.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| 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".