The effect of trastuzumab on pCR in locally advanced HER2-positive breast cancer.
Bibliographic record
Abstract
286 Background: Neoadjuvant therapy (NAT) is now standard of care for locally advanced breast cancer (LABC). Evidence shows that pathological complete response (pCR) predicts for disease free and overall survival. The pCR rates for LABC vary widely in the literature but prognosis still remains poor for this group of pts. Increases in pCR have been reported in clinical trials with the addition of trastuzumab (T) but these studies have predominantly included operable pts. The aim of this study was to evaluate whether the addition of T to NAT has improved the rates of pCR in HER2+ LABC pts at our center. Methods: Pts from the LABC prospective database at the Sunnybrook Odette Cancer Center in Toronto were included if they had confirmed HER2+ LABC [primary tumors greater than 5cm (T3) with skin or chest wall involvement (T4) or with matted axillary adenopathy (N2)]. Two cohorts of LABC pts, pre-T and post-T groups were compared for baseline characteristics and pCR. Chi square tests and p values were used for comparing proportions. Results: 43 pts were diagnosed between Jan 2002 to Dec 31, 2006 who had HER2+ breast cancer and received NAT without T (pre-T cohort). 17 HER2+ pts were diagnosed with LABC between Jan 1, 2007 to Dec 31, 2009 who received neoadjuvant T (post-T cohort). Baseline characteristics were similar in two cohorts (Table) except more pts in pre-T cohort received neoadjuvant hormonal therapy. The rate of pCR in the pre-T cohort was 9.3% and in the post-T cohort 29% (p value=0.02). Conclusions: The pCR rate dramatically improved in our LABC patients with the addition of T to NAT. The pCR rates still remain lower than in published clinical trials likely reflecting the more advanced nature of LABC in clinical practice. [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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".