Bibliographic record
Abstract
Since the initial description of the HER2 proto-oncogene as a poor prognostic factor in breast cancer in 1987, to the first randomized trial of a monoclonal antibody directed against HER2 in combination with chemotherapy for the treatment of metastatic HER2-positive breast cancer published in 2001, to the American Society of Clinical Oncology (ASCO) 2005 Annual Meeting in which we saw the unprecedented collective presentations demonstrating the dramatic benefit of trastuzumab in the adjuvant setting-the clinical landscape of HER2-overexpressing breast cancer has forever changed. More recently, there has been increasing use of preoperative chemotherapy and anti-HER2 targeted therapies in primary operable HER2 disease in the research domain and in clinical practice. In the next few years, we will see if dual adjuvant anti-HER2 antibody inhibition produces clinically significant improvements in outcome; understand if there is a role of small molecule inhibitors of the HER family of receptors either in combination or sequential to trastuzumab; further refine the relationship between pathologic complete response (pCR) and long-term clinical outcomes; and find predictive biomarkers to identify cohorts of patients that may need differential combinations and/or durations of anti-HER2 therapies.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".