Sites of metastasis and survival in metastatic renal cell carcinoma (mRCC): Results from the International mRCC Database Consortium (IMDC).
Bibliographic record
Abstract
642 Background: Across a variety of malignancies, sites of metastatic involvement are known to be associated with differences in survival. We sought to characterize the frequency and survival of patients with different sites of metastases in mRCC. Methods: Patients with mRCC starting treatment between 2002-2019 were identified and sites of metastatic involvement at time of systemic therapy initiation were documented. The primary outcomes of interest were prevalence of metastatic site involvement and overall survival (OS). Multivariable Cox regression models were performed to adjust for imbalances in IMDC risk factors. Results: A total of 10,320 patients were included. Median age at diagnosis was 60, 73% were male, 87% had clear-cell histology and 80% underwent nephrectomy. The most common sites of metastases were: lung (71%), lymph nodes (49%), bone (36%), liver (21%), adrenal (9%), brain (9%), pancreas (5%), pleura (4%) and thyroid (0.6%). Survival by metastatic site and adjusted hazard ratios are presented in Table. Conclusions: Metastases to endocrine organs (pancreas, thyroid, adrenal) are infrequent but are associated with the longest median OS, whereas bone, liver, pleura and brain metastases are associated with median OS < 18 months. These benchmark values are useful for patient counseling and study design. Sites of metastatic involvement may reflect differences in underlying disease biology, and further work to investigate differences in immune, molecular and genetic profiles between metastatic sites is encouraged.[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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".