Metastatic Brain Tumors: To Treat or Not to Treat, and with What?
Bibliographic record
Abstract
A long time ago, metastatic brain tumors were often not treated and patients were only given palliative care. In the past decade, researchers selected those with single or 1-3 metastases for more aggressive treatments like surgical resection, and/or stereotactic radiosurgery (SRS), since the addition of whole brain radiotherapy (WBRT) did not increase overall survival for the vast majority of patients. Different studies demonstrated significantly less cognitive deterioration in 0-52% patients after SRS versus 85-94% after WBRT at 6 months. WBRT is the treatment of choice for leptomeningeal metastases. WBRT can lower the risk for further brain metastases, particularly in tumors of fast brain metastasis velocity, i.e. quickly relapsing, often seen in melanoma or small cell lung carcinoma. Important relevant literature is quoted to clarify the clinical controversies at point of care in this review. Synchronous primary lung cancer and brain metastasis represent a special situation whereby the oncologist should exercise discretion for curative treatments, with reported 5-year survival rates of 7.6%-34.6%. Recent research suggests that those patients with Karnofsky performance status less than 70, not capable of caring for themselves, are less likely to derive benefit from aggressive treatments. Among patients with brain metastases from non-small cell lung cancer (NSCLC), the QUARTZ trial (Quality of Life after Radiotherapy for Brain Metastases) helps the oncologist to decide when not to treat, depending on the performance status and other factors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".