Survival and Complication Rates of Metastasectomy in Patients With Metastatic Renal Cell Carcinoma Treated Exclusively With Targeted Therapy: A Combined Population-based Analysis
Bibliographic record
Abstract
AIM: This study analyzed the effect of metastasectomy on overall mortality (OM) and perioperative outcomes in patients with metastatic renal cell carcinoma (mRCC) treated exclusively with targeted therapy. MATERIALS AND METHODS: Using the Surveillance, Epidemiology, and End Results (SEER) database (2006-2015), Kaplan-Meier analyses and multivariable Cox regression models tested for OM. Using the National Inpatient Sample (NIS) database (2006-2015), complication rates and in-hospital mortality were evaluated. RESULTS: Within the SEER database, 437 (12.2%) out of 3,654 patients underwent metastasectomy. Metastasectomy was associated with lower OM risk (median survival 11 vs. 9 months, hazard ratio=0.83; p=0.002). Within the NIS database, 351 such patients were identified. Complications and in-hospital mortality were 55.0% and 4.6%, respectively. CONCLUSION: Metastasectomy in patients with mRCC treated exclusively with targeted therapy is associated with lower OM risk, however, based on short duration of expected survival. Complications and in-hospital mortality rates are not negligible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".