Positive surgical margins may not affect the survival of patients with renal cell carcinoma after partial nephrectomy: A meta-analysis based on 39 studies
Bibliographic record
Abstract
Background: So far, whether positive surgical margin(PSM) has adverse effects on the prognosis of patients is still controversial, so we designed this study to systematically evaluate the effect of PSM on the prognosis of patients with renal cell carcinoma (RCC) after partial nephrectomy (PN). Methods: On the basis of three electronic databases (PubMed, Embase and the Cochrane Library) up to May 2022, all case-control studies (CCSs) comparing the effects of PSM and negative surgical margin (NSM) after PN on the oncological results of RCC patients were included. Two evaluators independently conducted a systematic literature search and extracted the data we needed. The methodological quality of all studies was evaluated by the modified Newcastle-Ottawa scale. The odds ratio (OR) was used to describe the results for dichotomous variables, and the meta-analysis was conducted using Cochrane Review Manager 5.2 and Stata 14.2. Results: A total of 39 studies involving 21461 patients were included in our meta-analysis. The pooled results showed that the rates of tumor recurrence (OR 3.93, 95% CI 2.95-5.24; p < 0.00001) and metastasis (OR 4.63, 95% CI 3.11-6.88; p < 0.00001) in the PSM group were significantly higher than those in the NSM group. However, there were no significant differences in the rates of all-cause death (OR 1.35, 95% CI 0.92-1.99; p = 0.13) or cancer-specific death (OR 0.99, 95% CI 0.51-1.94; p = 0.99) between the two groups. In addition, subgroup analyses were carried out according to different average follow-ups, which revealed similar results. Conclusion: Insignificant differences in survival between the PSM and NSM groups were observed, although significant differences in recurrence and metastasis in the PSM group were reported. Our study supported that close monitoring might be another effective choice for patients with PSM after PN. Considering the possible limitations, we recommended cautious interpretation of our results.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.077 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".