Characterization of patients with brain metastases referred to palliative care
Bibliographic record
Abstract
PURPOSE: In this study, we aimed to assess the clinical characteristics, reasons for referral, and outcomes of patients with brain metastases (BM) referred to the supportive care center. METHODS: Equal numbers of patients with melanoma, breast cancer, and lung cancer with (N = 90) and without (N = 90) BM were retrospectively identified from the supportive care database for study. Descriptive statistics were used to analyze demographic, disease, and clinical data. Kaplan Meier method was used to evaluate survival outcomes. RESULTS: While physical symptom management was the most common reason for referral to supportive care for both patients with and without BM, patients with BM had significantly lower pain scores on ESAS at time of referral (p = 0.002). They had greater interaction with acute care in the last weeks of life, with higher rates of ICU admission, emergency room visits, and hospitalizations after initial supportive care (SC) visit. The median survival time from referral to Supportive Care Center (SCC) was 0.90 years (95% CI 0.73, 1.40) for the brain metastasis group and 1.29 years (95% CI 0.91, 2.29) for the group without BM. CONCLUSIONS: Patients with BM have shorter survival and greater interaction with acute care in the last weeks of life. This population also has distinct symptom burdens from patients without BM. Strategies to optimize integration of SC for patients with BM warrant ongoing study.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".