Relationship between survival to breast cancer and level of HER2 expression by immunohistochemistry.
Bibliographic record
Abstract
e13078 Background: Central review of NSABP B-31 showed that some central-review HER2-negative patients benefited from trastuzumab, but survival according to different levels of HER2 expression by immunohistochemistry (IHC) is unknown. The aim of the present study was to examine the survival of trastuzumab-naïve patients with breast cancer according to the level of HER2 expression by IHC. Methods: This was a retrospective study of all women who were treated for an invasive breast cancer at the Centre des maladies du Sein Deschênes-Fabia (Quebec City, Province of Quebec, Canada) between July 1999 and December 2010. Patients were grouped according to the HER2 status by IHC of their primary cancer (0 vs. 1+ vs. 2+ vs. 3+). Survival was obtained from the death registry of the Ministry of Health. Follow-up was censored on December 31st, 2012. Results: During the study period, 2571 patients with invasive breast cancer, with available HER2 status by IHC and survival data, and naïve to anti-HER2 therapy were treated at our center. Both 5- and 9-year overall survival (OS) decreased with increasing HER2 expression by IHC (9-year OS: 0: 86.2% vs. 1+: 71.3% vs. 2+: 73.9% vs. 3+: 69.3%; unadjusted log-rank test: P < 0.0001; adjusted P = 0.04 for 3+). Both 5- and 9-year breast cancer-specific survival (BCSS) decreased with increasing HER2 expression by IHC (9-year BCSS: 0: 90.7% vs. 1+: 81.8% vs. 2+: 77.0% vs. 3+: 75.4%; unadjusted log-rank test: P < 0.0001; adjusted P = 0.04 for 3+). Conclusions: Survival among trastuzumab-naïve breast cancer patients seems to follow a continuum across HER2 expression by IHC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".