Population-based outcomes of patients (pt) with early-stage HER2-positive breast cancer treated with adjuvant trastuzumab (T).
Bibliographic record
Abstract
e11079 Background: Phase III trials have shown clinical efficacy of T when combined with chemotherapy in HER2-positive early stage breast cancer, decreasing recurrence by 50% and increasing survival by 30%. 15-20% of early stage breast cancers demonstrate amplification of the HER2 gene, which is associated with a poor prognosis. The aims of this study were to evaluate the clinical effectiveness of T, and explore potential prognostic factors. Methods: Pts with stage I-III breast cancer overexpressing HER2 from 2005 to 2010, assessed in Newfoundland and Labrador’s cancer centre were retrospectively identified from the Provincial Tumour Registry. Pt, treatment, and tumour characteristics were extracted. Kaplan-Meier curves were used for survival analysis, and Cox Proportional Hazards Models were used to identify prognostic factors and evaluate their impact on outcomes. Results: A total of 148 pts were identified. The median age was 56 years, and 76% received T. At a median follow-up of 25 months, overall survival (OS) was 97% (p=0.0002), and disease-free survival was 96% (p<0.00) for pts receiving T. Younger age, smaller tumour size, and lymph node negativity were favorable prognostic factors. There was an 83% decrease in risk of breast cancer recurrence in the patients receiving T. Discontinuation of T occurred in 6.2% of patients due to a decreased ejection fraction. Conclusions: This population-based analysis demonstrates T’s favorable impact on 25-month DFS, OS, and safety. This adds to the body of literature, showing clinical effectiveness and tolerability of T. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".