Factors affecting complications according to the modified Clavien classification in complete supine percutaneous nephrolithotomy
Bibliographic record
Abstract
INTRODUCTION: An increase in percutaneous nephrolithotomy (PCNL) has been accompanied by an increase in complications. We identified the parameters affecting the severity of complications using the modified Clavien classification (MCC). METHODS: From 2008 to 2013, 330 patients underwent complete supine PCNL using subcostal access, one-shot dilation, rigid nephroscopy, and pneumatic lithotripsy. We assessed the impact of the following factors on complication severity based on the MCC: age, gender, body mass index, hypertension, diabetes, previous stone surgery and extracorporeal shock wave lithotripsy, preoperative hemoglobin, renal dysfunction (creatinine >1.4 mg/dL), preoperative urinary tract infection, anatomic upper urinary tract abnormality (AUUTA), significant (moderate-severe) hydronephrosis, stone-related parameters (opacity, number, burden, location, staghorn, complex stones), anesthesia type, kidney side, imaging and calyx for access, tract number, tubeless approach, operative time, postoperative hemoglobin, and hemoglobin drop and stone-free results. RESULTS: The complication rate was 19.7% (MCC: 0=80.3%, I=6.4%, II=11.2%, ≥III=2.1%). On univariate analyses, only the following factors affected MCC: gender, preoperative hemoglobin, AUUTA, significant hydronephrosis, imaging for access, calyx for access, tract number, postoperative hemoglobin, hemoglobin drop and stone-free result. Renal dysfunction was accompanied by higher complications, yet the results were not statistically significant. Multivariate logistic regression analysis demonstrated renal dysfunction, absence of significant hydronephrosis, AUUTA, multiple tracts, lower postoperative hemoglobin, and higher postoperative hemoglobin drop as the significant parameters which affected MCC and predicted higher grades. The paper's limitations include a low number of cases in the higher Clavien grades and some subgroups of variables, and not applying some techniques due to surgeon preference. INTERPRETATION: Many of the complete supine PCNL complications were in the lower Clavien grades and major complications were uncommon. Renal dysfunction, AUUTA, significant hydronephrosis, tract number, postoperative hemoglobin, and hemoglobin drop were the only factors affecting MCC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".