Laparoscopic Versus Open Nephroureterectomy for Upper Tract Urothelial Carcinoma: A Systematic Review and Meta-Analysis of Propensity-Score Matched Studies
Bibliographic record
Abstract
BACKGROUND: The effectiveness of laparoscopic nephroureterectomy (LNU) vs open nephroureterectomy (ONU) for upper tract urothelial carcinoma (UTUC) is unclear. METHODS: We conducted a meta-analysis of studies based on propensity score-matched cohorts to compare the surgical and oncological outcomes of LNU and ONU in UTUC patients. A literature search was conducted on PubMed, Embase, and Cochrane Library until July 12, 2023. The Newcastle-Ottawa Scale was utilized to assess the quality of eligible studies. Measurements of surgical and oncological outcomes were extracted and pooled including mean difference (MD), risk ratio (RR), hazard ratios (HR), and 95% confidence intervals (CI). RESULTS: Five high-quality retrospective studies were included, totaling 6422 patients; 2080 (32.4%) underwent LNU, and 4342 (67.6%) underwent ONU. With respect to surgical outcomes, patients in the LNU group experienced less estimated blood loss and had shorter hospital stay than those in the ONU group, but there was no significant difference in complication rates and operation time. In regard to oncological outcomes, there were no significant differences between the LNU and ONU groups in 3-year overall survival (OS) and cancer-specific survival (CSS). However, 3-year intravesical recurrence free survival (IVRFS) was worse in the LNU group compared to the ONU group. CONCLUSION: LNU was associated with less estimated blood loss and shorter hospital stays than ONU, but there were no differences in OS and CSS between the surgical modalities. Nonetheless, LNU might result in poorer IVRFS than ONU.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| 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 | 0.001 |
| 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".