Preoperative patient optimization for endourological procedures: the current best clinical practice
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite technological advancements in endourological surgery, there is room for improvement in preoperative patient optimization strategies. This review updates recent best clinical practices that can be implemented for optimal surgical outcomes. RECENT FINDINGS: Outcome and complication predictions using novel scoring systems and techniques have shown to assist clinical decision-making and patient counseling. Innovative preoperative simulation and localization methods for percutaneous nephrolithotomy have been evaluated to minimize puncture-associated adverse events. Novel antibiotic prophylaxis strategies and further recognition of risk factors that attribute to postoperative infections have shown the potential to minimize perioperative morbidity. Accumulating data on the roles of preoperative stenting and selective oral alpha-blockers adds evidence to the current paradigm of preventive measures for ureteral injury. SUMMARY: Ample tools and technologies exist that can be utilized preoperatively to improve surgical outcomes. The combination of these innovations, along with validation in larger-scale studies, presents the cornerstone of future urolithiasis management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".