Impact of tumor size on survival outcome in metastatic renal cell carcinoma patients (mRCC) treated with targeted therapy.
Bibliographic record
Abstract
667 Background: Recent research suggested that patients (pts) with small renal masses (4cm or less) were at low risk of disease recurrence after surgery. The impact of tumor size on survival in mRCC patients treated with targeted therapy (TKI) is unclear. Methods: Two cohorts were identified from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC). Cohort 1 pts had initial nephrectomy for M0 RCC and subsequently developed metastasis during follow-up. Cohort 2 pts presented with de novo metastasis with or without cytoreductive nephrectomy. Cox regression was performed to assess the associations of primary tumor size (≤4 vs > 4cm) and overall survival (OS) on first line TKI, adjusted for histology, sarcomatoid features, tumor stage, number of metastasis, IMDC risk groups and age at TKI initiation. Results: 4089 pts with mRCC treated with first line TKI had primary tumor size data available. Patient characteristics were generally balanced between tumor size groups (≤4 vs > 4cm), except pts with ≤4cm tumors were more likely to have single metastasis (29% vs 18%, p = 0.001) and less likely to have IMDC poor risk (32% vs 39%, p = 0.04) in pts from cohort 2. For pts from cohort 1, tumor size at initial nephrectomy did not impact OS after TKI initiation (p = 0.689). However, in pts presenting with de novo metastasis (cohort 2), small primary tumors were associated with improved OS after TKI initiation, but only in T1-2 tumors (Table). Conclusions: Tumor size impacts survival outcome with targeted therapy in mRCC patients presenting with de novo metastasis and T1-2 disease. This may need to be taken in consideration in clinical trial designs. [Table: see text]
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".