SURG-13. Multiplicity does not affect outcomes in patients with surgically treated brain metastases
Bibliographic record
Abstract
Abstract Background Having multiple brain lesions has been considered a negative prognostic factor in patients with brain metastases. The role of surgery in the management of these patients remains a matter of debate. Methods We retrospectively reviewed our patients who underwent surgical resection of brain metastases from January 2018 to December 2019, and examined outcomes including overall survival (OS), progression free survival (PFS) and rates of local failure. Results We identified 130 patients who underwent surgical resection as the primary treatment modality of brain metastases. At the time of surgery, 117 patients harbored 1–3 lesions, 13 had more than 3 lesions. Overall survival at two years for our entire cohort was 46%. The difference in OS between patients with > 3 metastases (21%) and 1–3 metastases (49%) was not statistically significant (HR=1.34, 95% CI: 0.67–2.68, p=0.41). Similarly, 27% of patients had PFS at two years, with 25% in the multiple metastases group and 28% in the comparison group (HR=1.19, 95% CI: 0.63–2.23, p=0.59). Additionally, 32% of patients overall experienced local failure at two years and there was no significant difference between patients with >3 metastases (15%) and those with fewer (33%) (HR=0.68, 95% CI: 0.21–2.19, p=0.52). A multivariate regression model examining multiple preoperative features revealed large tumor volume to be the only independent predictor of limited OS (p = 0.017) and PFS (p = 0.023), and local failure (p = 0.031). Conclusions In carefully selected patients, surgical resection is a reasonable management option for patients with multiple brain metastases.
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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 | 0.001 |
| 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) | 0.000 | 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".