SYST-03 INCIDENCE AND SURVIVAL OF PATIENTS WITH INTRACRANIAL METASTATIC DISEASE AND ERBB2-POSITIVE GASTROINTESTINAL CANCERS: A RETROSPECTIVE COHORT STUDY
Bibliographic record
Abstract
Abstract BACKGROUND Intracranial metastatic disease (IMD) is a mortality-driving complication of gastrointestinal (GI) cancers. In breast cancer, ERBB2 positivity is associated with shorter overall survival (OS) and increased risk of IMD, and while ERBB2 status is relevant in primary GI cancer, no study has directly assessed the relationship of ERBB2 status and IMD in these patients. METHODS Records for adult patients with GI cancer and IMD, treated with ERBB2-therapy between 2005 and 2018 were retrieved from ICES. Baseline characteristics were compared between subcohorts stratified by IMD and ERBB2 statuses. Kaplan-Meier and Cox regression analyses were performed to estimate survival. RESULTS Records for 99,256 patients with GI cancer were collected, and IMD was diagnosed in 2002 patients. The highest IMD incidence rate was among patients with esophageal cancer (5.5%). Among patients with ERBB2+ disease, 306 had gastric (9 IMD), 168 esophageal (15 IMD), and 17 colorectal cancer. Diagnosis of IMD was associated with shorter OS among patients with colorectal (HR 3.0; 95% CI 2.9–3.2), gastric (HR 1.7; 95% CI, 1.5–1.9), and esophageal cancers (HR 1.2; 95% CI, 1.1–1.4). Post-IMD ERBB2-targeted therapy was not associated with OS among patients with ERBB2+ esophageal (HR 0.5; 95% CI, 0.2–1.2; n = 15) or gastric cancer (HR 0; 95% CI 0–Inf; n = 9). CONCLUSION Our study assessed patients with ERBB2+ GI cancer and IMD. Diagnosis of IMD was associated with shorter survival in gastric, esophageal, and colorectal cancers. Post-IMD ERBB2 therapy was not associated with OS, and IMD diagnosis was associated with prolonged survival in patients with stage 4 ERBB2+ disease, although interpretation of these results is complicated by small sample size and selection bias. Our results motivate increased reporting and inclusion of patients with ERBB2+ GI cancers in clinical trials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".