Prognostication of Brain-Metastasized Patients Receiving Subsequent Systemic Therapy: A Single-Center Long-Term Follow-Up
Bibliographic record
Abstract
BACKGROUND: Survival of patients with brain metastases (BMs) is poor. It has become clear that targeted therapy has an effect on BMs and patient' prognosis. The question remains which patients benefit from additional systemic therapy. This assumption was evaluated in a large single-center cohort. METHODS: = 200). Prognosis, using CERENAL, disease-specific graded prognostic assessment (DS-GPA), and Radiation Therapy Oncology Group recursive partitioning analysis (RTOG RPA), was evaluated. RESULTS: Ninety-three (46.5%) patients received at least one additional line of systemic therapy subsequent to the diagnosis of their BMs. The median overall survival (OS) was 6.3 months. Having received subsequent systemic therapy resulted in a more favorable OS (10.4 versus 3.9 months). Interestingly, using dichotomized scores, CERENAL showed prognostic properties in all patients for disease-specific survival on multivariate analysis, whereas RTOG RPA and DS-GPA were not withheld in the model. Lastly, only having a favorable DS-GPA resulted in prolonged progression-free survival for first systemic therapy following BM diagnosis. CONCLUSIONS: Receiving subsequent systemic therapy has a profound influence on outcome in patients with BMs, indicating the effect of systemic therapy on BMs. Use of the CERENAL brain prognostic score shows potential for further prognostication of patients with more favorable outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".