Predictors of urosepsis in struvite stone patients after percutaneous nephrolithotomy
Bibliographic record
Abstract
PURPOSE: This study aims to identify clinical factors that may predispose struvite stone patients to urosepsis following percutaneous nephrolithotomy (PCNL). MATERIALS AND METHODS: A retrospective review was conducted on patients who received PCNL for struvite stones. The Systemic Inflammatory Response Syndrome (SIRS) criteria and quick-Sepsis Related Organ Failure Assessment (q-SOFA) criteria were used to identify patients who were at an increased risk for urosepsis. Statistical analysis was performed using Fisher's exactness test, Wilcoxon rank test, and logistic regression. RESULTS: Chart review identified 99 struvite stone patients treated with PCNL. Post-operatively, 40 patients were SIRS positive (≥2 criteria) and/or q-SOFA positive (score ≥2). Using SIRS as an approximation for urosepsis, longer operative times (p<0.001), higher pre-operative white blood cell counts (p=0.01), greater total stone surface area (p<0.0001), and pre-operative stenting (OR, 5.75; p=0.01) were identified as independent risk factors for urosepsis. Multivariate analysis demonstrated pre-operative stenting (OR, 1.46; p=0.01) to be a risk factor. With q-SOFA, univariable analysis found that antibiotic use within 3 months prior to a PCNL (OR, 4.44; p=0.04), medical comorbidities (OR, 4.80; p=0.02), longer operative times (p<0.001), lengthier post-operative hospitalization (p<0.01), and greater total stone surface area (p<0.0001) were risk factors for urosepsis. Multivariate analysis revealed that bladder outlet obstruction (OR, 2.74; p<0.003) and pre-operative stenting (OR, 1.27; p=0.01) significantly increased odds of being q-SOFA positive. CONCLUSIONS: Several risk factors for urosepsis following PCNL for struvite stones have been identified. These risk factors should be taken into consideration in peri-operative care to mitigate the risks of urosepsis.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".