The impact of HER2 positivity on survival in metastatic gastric and GEJ cancer.
Bibliographic record
Abstract
17 Background: HER2 positive (+) gastric (GC) and GEJ adenocarcinoma (AC) has been associated with a worse survival outcome. This study examines the survival rate of patients (pts) with metastatic HER2 (+) GC and GEJ AC in the province of BC. Methods: Formalin fixed embedded tissue from pts with resected GC or GEJ AC from 2004−2011 were identified retrospectively through the BC Cancer Agency registry and prospectively for pts with a new diagnosis of advanced disease. Biopsies and resection samples were analyzed via previously validated methods. IHC scores of 3 were considered (+), 2 were equivocal and 0/1 were negative (−). 10% cut-off was used to determine (+) samples. Equivocal staining was considered (+) via FISH or SISH with a ratio of > 2.2 considered amplified. Pt characteristics were abstracted to an anonymized database. Kaplan meier curves were calculated to evaluate overall survival from time of diagnosis or relapse to death for pts with Her 2 (+) or (-) disease. Results: 164 of 294 pts were identified with metastatic or relapsed GC (49%) or GEJ (51%) AC without traztumumab treatment. 21 (13%) of pts were HER2 (+) either by IHC, FISH or SISH. See Table for pt characteristics. The HER2 (+) group was older (median age 68 yrs vs 60), more GEJ (62% vs 49% p=1.23), and a higher number of pts that did not receive treatment (52% vs. 27% p=5.09). Median survival was not significantly different among HER2 (+) and (−) disease (4.0 mo vs. 7.3 mo, p=0.089). Conclusions: The rate of HER2 positivity is similar to that seen in trial data. HER2 positivity trends to a worse prognosis but was not significant in this study. [Table: see text]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".