Orthopedic interventions and outcomes in patients with metastatic renal cell carcinoma: a systematic review
Bibliographic record
Abstract
Background: Despite advanced in systemic therapy for renal cell carcinoma (RCC), bone metastasis remains an adverse prognostic factor and major cause of mortality and morbidity. Orthopedic interventions may provide symptom relief, functional recovery, and survival benefit, yet the evidence is fragmented across heterogeneous studies. Objectives: To systematically review the outcomes of orthopedic surgical interventions in patients with symptomatic bone metastases from RCC. Design: Systematic literature review conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data sources and methods: A comprehensive search of Embase, MEDLINE, and the Cochrane Library was conducted for studies published between January 1, 2014, and January 30, 2024. Eligible studies included randomized controlled trials, prospective observational studies, and retrospective studies reporting on orthopedic interventions for RCC bone metastases. Primary outcomes included overall survival, reoperation rates, complication rates, and quality of life. Results: Of 2208 studies screened, 15 met the inclusion criteria. All were retrospective series or case series, limiting the strength of the evidence. Six studies focused on appendicular skeleton metastases, five on axial skeleton involvement, and four on both regions. Results were primarily reported narratively, with limited statistical analyses. Orthopedic surgical interventions-particularly when combined with targeted systemic therapy-were associated with longer overall survival. Among surgical approaches, complete metastasectomy was most consistently associated with improved survival compared with intralesional curettage and stabilization-only procedures. Conclusion: Although available data suggest that orthopedic surgery, particularly complete metastasectomy, may improve overall survival and quality of life in RCC patients with bone metastases, the evidence is limited to retrospective and narrative reports. Some studies also suggest that outcomes may be further enhanced when surgery is integrated with systemic therapy. Given the poor prognosis associated with bone involvement in RCC, prospective randomized studies are urgently needed to define optimal patient selection, standardize management strategies, and integrate surgery with systemic therapy in a multidisciplinary framework.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".