Cumulative incidence and survival outcomes of brain metastases in sarcoma: a large single center retrospective analysis
Bibliographic record
Abstract
BACKGROUND: The incidence and predictors of brain metastases (BrM) from sarcoma remain poorly characterized. We aimed to determine the cumulative incidence (CuI) and risk factors for BrM. METHODS: We retrospectively analyzed data from all sarcoma patients who presented to our center (2006-2023). CuI was calculated from initial presentation to BrM, stratified by key variables. Univariable (UVA) and multivariable competing risk regression analyses (MVA) were conducted to identify risk factors. RESULTS: Among 5110 sarcoma patients, 117 developed BrM. CuI rates were 1.8%, 2.4%, and 2.9% at 24, 48, and 72 months, respectively, within a median onset of 17 months. On UVA, intrathoracic primary site, alveolar soft part (ASPS), epithelioid, intimal and Rhabdomyosarcoma histologies, and stage IV at diagnosis were associated with increased CuI, while age ≥59, retroperitoneal origin and liposarcoma were associated with decreased CuI. On MVA the following remained correlated to BrM incidence: intrathoracic primary (HR 5.13), ASPS (HR 4.2), age ≥59 years (HR 0.45) and liposarcoma (HR 0.11); 44.3% presented with solitary BrM. Median survival post-BrM diagnosis was 6 months. CONCLUSION: BrM risk in sarcoma varies by age, histology, and tumor location. Solitary metastases were common in our BrM cohort, and OS post-BrM was poor.
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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 | 0.002 |
| 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) | 0.001 | 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".