CMET-25. COMPARATIVE EFFICACY OF TREATMENT PARADIGMS FOR BRAIN METASTASES – A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Abstract BACKGROUND Brain metastases (BM) are prevalent in several primary cancers and are the most commonly diagnosed intracranial tumours in adults. The aim of this systematic review was to critically evaluate and compare existing management paradigms for BMs from all primary tumour sites. METHODS We searched MEDLINE, EMBASE, Web of Science, ClinicalTrials.gov, and CENTRAL for randomized controlled trials (RCTs) published until October 2018 and selected comparative RCTS with >50 patients. The primary outcomes of interest were overall survival (OS) and progression-free survival (PFS). RESULTS A screen of 3188 abstracts yielded 49 RCTs with 8122 patients. The median sample size was 103 (range 39–779). 17/49 RCTs included lung cancer patients, 3/49 breast cancer, 2/49 melanoma, and 27/49 all primary tumour sites. Most studies were phase II/III superiority, open-label, parallel trials. All included patients >18 years of age with at least 1 BM proven on CT/MRI. Most included patients with favorable performance status (KPS >70 or ECOG 0–2). Interventions compared included targeted therapies (e.g. EGFR inhibitors, 7/49), chemotherapeutic regimens (28/49), WBRT (43/49), SRS (12/49), and surgical resection (3/49). A statistically significant difference in OS was reported by 7 studies, and in PFS by 8 studies. Of these, 3 showed improved PFS or OS on addition of temozolomide to WBRT, and 2 showed improved PFS in EGFR-positive patients treated with EGFR inhibitors. The incidence of serious adverse events was not substantially different between treatment arms in most studies. CONCLUSIONS Though the majority of studies did not report a benefit to OS or PFS, temozolomide + WBRT showed a benefit in 3 studies, and improvement in PFS was seen in EGFR-positive patients treated with EGFRi. Given the varying treatment approaches in practice today, these data support the need for a meta-analysis to accurately characterize the efficacy of existing treatment options for specific patient populations.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".