Evaluating the acceptability of an online patient decision aid for the surgical management of lower urinary tract symptoms secondary to benign prostatic hyperplasia
Bibliographic record
Abstract
INTRODUCTION: The growing number of surgical options available to treat benign prostatic hyperplasia (BPH), may overwhelm patients and urologists when deciding on an optimal treatment. Therefore, we developed an online patient decision aid (PtDA) that includes all guideline-approved surgical modalities. The objective of this study was to assess the acceptability of the PtDA among former BPH surgery patients and urologists that treat BPH surgically. METHODS: The International Patient Decision Aids Standards were used to develop a PtDA that includes monopolar transurethral resection of the prostate (TURP), bipolar TURP, GreenLight photovaporization, endoscopic enucleation of the prostate, Rezum, Urolift, Aquablation, open retropubic prostatectomy, and robotic simple prostatectomy as management options. Eleven urologists that regularly treat BPH and 19 patients who received BPH surgery were recruited. Alpha-testing was performed using a validated acceptability scoring system. RESULTS: For all sections of the PtDA, most urologists agreed that the language used was easy to follow (91.9%), that the amount of information provided was adequate (63.6%), that the length of the PtDA was appropriate (63.6%), and that the outcomes reported were correct (81.8%). All 19 patient participants agreed that the language used was easy to follow, and most found that the amount of information provided was adequate (84.2%), that the length of the PtDA was appropriate (84.2%), and that the outcomes reported were well-explained (89.5%). CONCLUSIONS: Our PtDA was found to be acceptable among urologists and patients. These results demonstrate that most of the participants either recommend the use of this tool or plan to incorporate it in their clinical practice.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".