Value of preoperative urine white blood cell and nitrite in predicting postoperative infection following percutaneous nephrolithotomy: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: To evaluate to what degree preoperative urine white blood cell (WBC) and urine nitrite (NIT) values are predictive of postoperative infections following percutaneous nephrolithotomy (PCNL). METHODS: A systematic literature search was performed of the PubMed, Embase, Cochrane Library, Wanfang Data, National Knowledge Infrastructure (CNKI), and China Science and Technology Journal Database (CSTJ or VIP) online databases to identify relevant studies that examined the predictive value of urine WBC or NIT as risk factors for post-PCNL infection, and the search was finished on February 28, 2020. Two independent reviewers screened the relevant studies, extracted necessary data from the eligible case-control studies (CCS), and assessed the quality of included studies through the Newcastle-Ottawa scale (NOS). RevMan 5.3 software and the Stata 16.0 software were used to complete the statistical analysis of data. Results are expressed as odds ratio (OR) with 95% confidence intervals (CIs). RESULTS: : OR =7.81, 95% CI: 5.44-11.21, P<0.001) in preoperative tests were identified as independent risk factors for postoperative infections following PCNL. CONCLUSIONS: In summary, as risk factors for postoperative infections, the presence of preoperative urine WBC+ and NIT+ should be evaluated as part of clinical procedure, in order to reduce infections of PCNL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".