Image_1_The Impact of Diabetes on the Prognosis of Upper Tract Urothelial Carcinoma After Radical Nephroureterectomy: A Systematic Review and Meta-Analysis.tif
Bibliographic record
Abstract
Background<p>Studies have reported that diabetes is related to the prognosis of upper tract urothelial carcinoma (UTUC) after radical nephroureterectomy (RNU), but this conclusion is still controversial. Here, we performed a meta-analysis to comprehensively explore the association between diabetes and UTUC prognosis.</p>Methods<p>In November 2020, we searched PubMed, Web of science and the Cochrane Library to find relevant studies that evaluated the effect of diabetes on the prognosis of UTUC. The Newcastle Ottawa Scale was used to assess the quality of the literature. Review Manager 5.3 was used to pool cancer-specific survival (CSS), overall survival (OS), recurrence-free survival (RFS) and intravesical recurrence (IVR).</p>Results<p>A total of 10 studies with 11,303 patients were included in this meta-analysis. Our pooled results showed that diabetes did not affect the survival outcome of UTUC, including CSS (HR: 1.33, 95% CI: 0.89-1.98; P = 0.16), OS (HR: 1.18, 95% CI: 0.77-1.80; P = 0.45) and RFS (HR: 1.37, 95% CI: 0.91-2.05; P = 0.13). However, diabetes increased the risk of IVR of UTUC patients (HR: 1.26, 95% CI: 1.11-1.43; P = 0.0004).</p>Conclusion<p>Although diabetes has no significant impact on the survival outcomes of UTUC after RNU, it increases the risk of IVR. Therefore, special attention should be paid to monitoring the IVR for UTUC patients with diabetes and the necessity of appropriate intravesical adjuvant treatment when needed.</p>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.688 | 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".