The incidence and risk factors of urinary tract infections in patients undergoing bladder tumor resection: a systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: To summarise the available evidence concerning the incidence and risk factors of UTIs in bladder cancer patients after surgery. METHODS: Systematic searches were conducted on PubMed, Embase, Web of Science, Cochrane Library, CINAHL, the China National Knowledge Base Database (CNKI), Wanfang Database, Vips Database (VIP), and the China Biomedical Database (Sinomed). These searches encompassed literature from the inception of each database up to March 2025. This study adhered rigorously to the PRISMA guidelines. The quality of the studies included in the review was assessed using the Joanna Briggs Institute (JBI) Centre for Evidence-Based Health Care in Australia and the Newcastle-Ottawa Scale. RESULTS: A total of 19 original studies were included in this analysis, encompassing 5,905 patients. The meta-analysis results indicated that the incidence of UTIs in patients with bladder tumor resection was 22.3% [95% CI (17.7, 27.3)]. The identified risk factors for UTIs in patients with bladder cancer after surgery include diabetes, age, preoperative catheter indwelling, Use of antibiotics before surgery, and Operation time≥90 min. CONCLUSIONS: UTIs are higher in patients who have undergone bladder tumor resection. Clinical staff should prioritize preoperative assessment and risk stratification for UTIs. They must adhere to established guidelines and recommendations regarding the prophylactic use of antibiotics before surgery, maintain strict control of patients' blood sugar levels, and manage catheters meticulously to minimize the risk of UTIs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it