Multiplicity does not significantly affect outcomes in brain metastasis patients treated with surgery
Bibliographic record
Abstract
Abstract Background Brain metastasis quantity may be a negative prognostic factor for patients requiring resection of at least one lesion. Methods We retrospectively reviewed patients who underwent surgical resection of brain metastases from July 2018 to June 2019 at our institution, and examined outcomes including overall survival (OS), progression free survival (PFS), and rates of local failure (LF). Patients were grouped according to the number of metastases at the time of surgery (single vs multiple). Results We identified 130 patients who underwent surgical resection as the initial treatment modality. At the time of surgery, 87 patients had only one lesion (control) and 43 had multiple (>1). Two-year OS for the entire cohort was 46%, with equal rates in both the multiple metastases group and the control group (P = .335). 2-year PFS was 27%; 21% in the multiple metastases group and 31% in the control group (P = .766). The rate of LF at 2 years was 32%, with equal rates in both the multiple lesion group and control group (P = .889). On univariate analysis, multiplicity was not significantly correlated to OS (HR = 0.80, 95% CI: 0.51–1.26, P = .336), PFS (HR = 1.06, 95% CI: 0.71–1.59, P = .766) or LF (HR = 1.06, 95% CI: 0.57–1.97, P = .840). Multivariate analysis revealed preoperative tumor volume of the resected lesion to be the single correlate for OS (P = .0032) and PFS (P = .0081). Conclusions Having more than one metastasis does not negatively impact outcomes in patients treated with surgery. In carefully selected patients, especially those with large tumors, surgery should be considered regardless of the total number of lesions.
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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 | 0.003 |
| 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".