Can incomplete metastasectomy impact renal cell carcinoma outcomes? A propensity score matching analysis from a prospective multicenter collaboration
Bibliographic record
Abstract
Objective: To evaluate the role of incomplete metastasectomy (IM) for patients with metastatic renal cell carcinoma (mRCC) on overall survival (OS) and time to introduction of first-line systemic therapy. Methodology: Patients diagnosed with mRCC between Jan 2011 and Apr 2019 in 16 centers were selected from the Canadian Kidney Cancer information system database. We included mRCC patients who had prior nephrectomy and had received an IM (resection of at least 1 metastasis) or no metastasectomy (NM). A propensity score matching was performed to minimize selection bias. Cox proportional hazards analysis was used to assess the impact of the metastasectomy while adjusting for potential confounders. OS was assessed by Kaplan-Meier analysis. Results: A total of 138 patients with mRCC underwent IM, while 1221 patients did not. On multivariate analysis, IM did not improve OS (hazard ratio [HR] 0.96, 95% CI 0.63 to 1.45, P = 0.836) However, subgroup analyses revealed IM improved OS compared with NM when lungs were the only site involved (median time to OS not reached versus 66 months, respectively; P = 0.014). Additionally, lung metastasectomy delayed the systemic therapy compared with NM (median 41 and 13 months, respectively, P = 0.014). IM of endocrine organs (thyroid, pancreas, adrenals) or bone metastases did not impact OS. Conclusion: The role of IM for mRCC is limited. Incomplete resection of lung metastases was associated with improved OS and delayed time to introduction of systemic therapy when lungs were the sole location of metastatic disease. Despite case-matching, unknown unadjusted confounders may explain the relationship between IM and survival in this analysis.
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".