Treatment response and survival outcome of patients with late relapse (LR) from renal cell carcinoma (RCC) in the era of targeted therapy.
Bibliographic record
Abstract
4578 Background: A small subset of localized RCC patients will experience disease recurrence ≥5 years after nephrectomy. Clinical outcome of patients with LR has not been well characterized. Methods: Patients with mRCC treated with targeted therapy were retrospectively characterized according to time to relapse. Replase was defined as diagnosis of recurrent metastatic disease >3 months after initial diagnosis. Patients with synchronous metastatic disease at presentation were excluded. Patients were classified as Early Relapsers (ER) if they recurred within 5 years while Late Relapsers (LR) recurred after 5 years. Demographics and outcomes were compared. Results: 1210 mRCC patients were identified; 903 (74.6%) with relapse within the first 5 years, 200 (16.5%) within >5-10 years, and 107 (8.8%) after 10 years (range 10-35 years). Baseline characteristics are presented in the Table. Overall response rates to targeted therapy were better in LR vs. ER (35% vs. 24%; p=0.009). LR patients had significant longer progression free- (10.7 vs. 8.5 months; log rank p=0.004) and overall survival (34.0 vs. 27.3 months; log rank p=0.003). Conclusions: One quarter of patients that eventually developed metastatic disease treated with targeted therapy relapsed over 5 years from initial diagnosis. The proportion of patients that relapse after five years is substantial. mRCC patients presenting with LR have more favorable prognostic features, treatment response, and overall survival. [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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".