Prognostic factors for overall survival with targeted therapy in Chinese patients with metastatic renal cell carcinoma
Bibliographic record
Abstract
INTRODUCTON: We wanted to identify the prognostic factors for overall survival (OS) in Chinese patients with metastatic renal cell carcinoma (mRCC) treated with first-line targeted therapy (sorafenib or sunitinib). METHODS: We retrospectively reviewed clinical data from 119 mRCC patients administered sorafenib or sunitinib at the Ruijin Hospital since 2007. OS rates were calculated by the Kaplan-Meier method. Each variable was investigated univariately and then multivariately using a stepwise algorithm. A multivariate Cox regression model analyzed baseline variables for prognostic significance. RESULTS: The mean patient age was 57 ± 12 years; 37 patients (31%) received sorafenib and 82 (69%) received sunitinib. The mean OS was 22.7 ± 15.6 months (range: 2.8- 68.7). OS rates at year 1, 3 and 5 were 74%, 57%, and 36%, respectively. Univariate analysis identified significant negative prognostic factors (p < 0.05) as Eastern Cooperative Oncology Group (ECOG) performance status ≥2, symptoms, no prior nephrectomy, microscopic necrosis, ≥2 metastatic sites, presence of liver, bone, or pancreas metastasis, hemoglobin less than the lower limit of normal(female <115 g/L, male <130 g/L), and serum alkaline phosphatase greater than the upper limit of normal (126 IU/L) at baseline, as well as a relative dose intensity of targeting agents in the first month (1M-RDI) of <50%. Multivariate analysis of OS identified 4 independent predictors: no symptoms, no bone or pancreas metastasis, and 1M-RDI of targeting agents (≥50%). CONCLUSIONS: With targeted therapy, there is some change in the prognostic factors for mRCC and target drug therapies (1M-RDI ≥50%) play an important role in the prognosis of mRCC. Continued progress in the identification of patient-specific prognostic factors for mRCC will require further advances in the understanding of tumour biology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".