The global, prevalence, and risk factors of postoperative fever after percutaneous nephrolithotomy: A systematic review and meta-analysis
Bibliographic record
Abstract
This study aimed to explore the global, prevalence, and risk factors of fever after percutaneous nephrolithotomy (PCNL) by conducting a systematic review and meta-analysis. The high-sensitivity searching was conducted without time limitation until Dec 30, 2020 in Web of Sciences, Scopus, and PubMed based on inclusion and exclusion criteria. The prevalence rates of fever and sepsis among patient undergoing PCNL are estimated 9.5% (95% confidence interval [CI]: 9.3%–9.7%), and 4.5% (95% CI: 4.2%–4.8%), respectively. Nephrostomy tube is used in 49.1% (95% CI: 47.9%–50.4%) of patient. The mean preoperative white blood cells (WBCs) of patients were 6.401 per microliter; 18.3% and 4.55% of patents were considered as the positive urinary culture, and pyuria, respectively. About 20.4% of patients suffered from residual stones. The odds ratio (OR) of fever in patients who suffering from diabetes mellitus was (OR: 4.62; 95% CI: 2.95–7.26), hydronephrosis was (OR: 1.04; 95% CI: 0.81–1.34), staghorn stones was (OR: 2.57; 95% CI: 0.93–7.11), and blood transfusion was (OR: 2.65; 95% CI: 1.62–4.35). Patients underwent PCNL who lied down in prone position were more likely to fever (OR: 1.23; 95% CI: 0.75–2.00) than patients in supine position. The current study showed that patients who suffer from diabetes mellitus, hydronephrosis, staghorn stones, nephrostomy tube or double-J stent, blood transfusion, and also ones who underwent prone position PCNL surgery have more likely to develop a postoperative fever after 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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".