Impact of early identification of brain metastases in metastatic renal cell carcinoma (mRCC).
Bibliographic record
Abstract
620 Background: Up to one-third of patients with mRCC can present with asymptomatic brain metastases (BM). Timely identification of BM allows for the delivery of early local interventions, which may lead to improved patient outcomes. To investigate the potential utility of routine intra-cranial imaging, we compared the outcomes of mRCC patients with asymptomatic versus symptomatic BM. Methods: Using the Canadian Kidney Cancer information system (CKCis) database, we identified mRCC patients diagnosed with BM between 2011 and 2018. This cohort was divided into two groups dependent on the presence or absence of neurological symptoms. Baseline patient demographics, clinico-pathological disease characteristics and survival data were extracted. Statistical analysis was through chi-square tests, analysis of variance and Kaplan-Meier method to characterize survival outcomes. Results: 269 mRCC patients with BM were identified with the majority presenting with symptomatic disease (n=163; 61%). No significant differences in clinico-pathological disease characteristics were identified. Median overall survival (OS) from mRCC diagnosis for asymptomatic patients was 33.4 months (interquartile range, IQR 27.8-64.4) versus 34.5 months (20.4-43.4) for symptomatic patients (p=0.35). Median OS from time of BM diagnosis revealed a trend favoring asymptomatic, as compared to symptomatic, patients [24.5 (17.6-24.9) vs. 13.1 months (9.0-20.6), p=0.06]. Factors associated with worse OS from time of BM diagnosis included presentation with symptomatic BM [hazard ratio, HR (95% CI): 1.40 (1.03-1.90), p=0.034] and International Metastatic Renal Cell Carcinoma Consortium Database (IMDC)-characterized intermediate/poor risk disease [HR (95% CI): 1.47 (1.01-2.13), p=0.045]. Conclusions: Routine intra-cranial imaging may lead to earlier identification of BM in mRCC. However, further investigation as to whether this practice improves survival is warranted.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".