Evaluation of Patient Treatment Preferences for 15 to 20 mm Kidney Stones: A Conjoint Analysis
Bibliographic record
Abstract
Introduction and Objective: Ureteroscopy (URS) and percutaneous nephrolithotomy (PCNL) are standard treatments for intermediate-size (15–20 mm) kidney stones but differ in their postoperative recovery, stone-free rates, and complication risks. We aimed to evaluate what affects patient treatment preferences. Methods: Patients with urinary stone disease completed a choice-based conjoint analysis exercise assessing four treatment attributes associated with URS and PCNL. A sensitivity analysis using a market simulator was performed, and the relative importance of each attribute was calculated. Differences in treatment preferences by demographic subgroup were assessed. Results: A total of 58 patients completed the conjoint analysis exercise. Stone-free rate was the most important treatment attribute, while the length of hospital stay and cosmesis were less important. Overall, sensitivity analysis based on market simulation scenarios predicted an almost equal preference for URS (52.4%) compared with PCNL (47.6%) for treatment of an intermediate-size stone. Older patients (>65 years old) expressed their stronger preferences for lower infection rates and shorter hospital stays, and were more likely to prefer URS (67.2%, 95% confidence interval [CI]: 52% to 82.5%) compared with younger patients (20–34 years old) (20.3%, 95% CI: 0% to 41.5%) who preferred higher procedure success rates and fewer repeat procedures. Conclusion: Conjoint analysis predicts nearly equal patient preference for URS or PCNL for the treatment of intermediate-size kidney stones. Older patients prefer the lower urinary tract infection risk and shorter hospital stay associated with URS, while younger patients prefer higher stone-free rates associated with PCNL. These results can help guide urologists in counseling patients and improve the shared decision-making process.
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".