Long-term oncologic outcomes of laparoscopic versus open partial nephrectomy.
Bibliographic record
Abstract
BACKGROUND: Most of the literatures on laparoscopic partial nephrectomy (LPN) versus open partial nephrectomy (OPN) focus on technical details and early or mid-term oncologic outcomes, reflecting that the approach is safe and provides midterm benefits compared with traditional open surgery. However, the difference of long-term oncologic outcome between LPN and OPN remains unclear. The aim of this meta-analysis was to evaluate the long-term oncologic outcome of LPN in the treatment of localized renal tumors compared with that of OPN. METHODS: A systematic search of electronic databases including Medline, Embase, and Cochrane library was conducted. Comparative studies reporting on long-term oncologic outcome of LPN versus OPN were regarded eligible. The odds ratio (OR) and its corresponding 95% confidence intervals (CI) were calculated for the oncologic outcomes. The methodologic quality of the included studies was evaluated using the strict criteria of the Newcastle-Ottawa scale. RESULTS: Six comparative studies (1495 participants including 555 LPN and 940 OPN) were included in the present study. There was no significant difference between LPN and OPN in 5-year overall survival (OS) rates (OR = 1.83, 95% CI (0.80, 4.19)), 5-year cancer specific survival (CSS) rates (OR = 1.09, 95% CI (0.62, 1.92)), and 5-year recurrence free survival (RFS) rates (OR = 0.68, 95% CI (0.37, 1.26)). CONCLUSION: The results of this meta-analysis revealed that there was no significant difference in long-term oncologic outcome between LPN and OPN for treatment of localized renal tumors.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.019 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".