Standardizing the reporting of percutaneous nephrolithotomy complications
Bibliographic record
Abstract
Accurate reporting of complications is an essential component to critical appraisal and innovation in surgery and specifically with percutaneous nephrolithotomy (PCNL). We review the evolution of complication reporting for PCNL and suggest future directions for innovation. A selective review was carried out using Pubmed. Key search terms and their combinations included percutaneous, anatrophic, nephrolithotomy, PCNL, complications, Clavien, Martin score, bleeding, bowel injury, perforation, fever, sepsis. The references from relevant papers and reviews as well as AUA and EAU guidelines were also scanned for inclusion. PCNL has become the procedure of choice for large renal stones owing to decreased morbidity over alternative procedures. Both common and rare complications have been described in large case series, small randomized controlled trials, and case reports in an unstandardized form. Although these reports have provided an informative starting point, a standardized complication reporting methodology is necessary to enable appropriate comparisons between institutions, time periods, or innovations in technique. The Clavien-Dindo grading system has become widely accepted in urology and has facilitated the study of PCNL complications. Future research should focus on adaptions of this system to render it more comprehensive and applicable to PCNL.
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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 | 0.138 | 0.405 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.030 | 0.024 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".