Estimating prognosis at the time of repeat whole brain radiation therapy for multiple brain metastases: The reirradiation score
Bibliographic record
Abstract
PURPOSE: Whole brain radiation therapy (WBRT) remains the standard of care for patients with multiple brain metastases, but more than half of treated patients will develop intracranial progression. Because there is no clear consensus on the optimal therapeutic approach, a prognostic index would be helpful to guide treatment options at progression. We explored whether the recursive partitioning analysis (RPA) score prior to repeat WBRT is predictive of survival. METHODS AND MATERIALS: This multi-institutional pooled analysis included patients with 2 or more brain metastases from any solid primary tumor that was treated with 2 courses of WBRT. Information on demographics, disease characteristics, and intervals between courses was collected. RPA class was abstracted or retrospectively assigned, and descriptive statistics calculated. Median survival (MS) was determined using the Kaplan-Meier method and compared using log rank tests. Univariate and multivariate analyses were performed via Cox regression analysis. RESULTS: = .001). On univariate and multivariate analyses, a Karnofsky Performance Status of <80, extracranial metastases, interval between courses <9 months, small cell lung cancer histology, and uncontrolled primary significantly correlated with shorter MS. By assigning a score of 1 to each of these factors, a new prognostic index was created, the reirradiation (ReRT) score. Survival on the basis of ReRT score grouping ranged from 2.2 to 7.2 months and demonstrated significant differences in MS. CONCLUSIONS: In the largest reported cohort to receive repeat WBRT, application of the RPA score was not predictive of MS. The new ReRT score is a simple tool based on readily available clinical information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".