Long-term Survival Among Patients With De Novo Human Epidermal Growth Receptor 2–Positive Metastatic Breast Cancer in Manitoba
Bibliographic record
Abstract
OBJECTIVES: Although metastatic breast cancer (MBC) is considered incurable, human epidermal growth receptor 2 (HER2)-directed therapy has improved outcomes significantly, with some patients experiencing durable responses to treatment. The aim of this study was to identify potential predictors of long-term survival (LTS) among patients with de novo HER2-positive MBC who received HER2-directed treatment. METHODS: Eligible patients from 2008 to 2018 were identified using the Manitoba Cancer Registry. LTS was defined as survival ≥5 years from the time of diagnosis. Univariate logistic regression models were performed to assess variables of clinical interest and the odds of LTS. Overall survival (OS) was defined as the time from diagnosis of MBC to death of any cause. OS was estimated using the Kaplan-Meier method with log-rank comparative analyses as a univariate analysis. A Cox proportional hazards model was used for OS estimates in a univariate analysis. RESULTS: A total of 62 patients were diagnosed with de novo HER2-positive MBC and received HER2-directed therapy. Eighteen (29%) achieved LTS. The median OS of the whole cohort was 50.2 months (95% CI: 28.6-not reached). Radiographic response to first-line treatment was associated with LTS; complete and partial responses were both associated with higher odds of LTS (odds ratio: 28.33 [95% CI: 2.47-4006.71, P = 0.0043] and odds ratio: 7.80 [95% CI: 0.7317-1072.00, P = 0.0972], respectively). The best radiographic response was associated with improved OS. CONCLUSIONS: Radiographic response to first-line HER2-directed therapy is a predictor for LTS in patients with de novo HER2-positive MBC. Larger studies are needed to identify patients who can safely discontinue HER2-targeted therapy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".