Bibliographic record
Abstract
PURPOSE OF REVIEW: Sequential anthracycline/taxane regimens are routinely used as neoadjuvant therapy (NAT) for locally advanced breast cancer. Unfortunately, the majority of patients do not achieve a pathological complete response (pCR). Efforts to improve pCR rates include the addition of novel targeted agents. The purpose of this article is to review recent developments in this area and to demonstrate the clinical and research advantages of a neoadjuvant platform for the evaluation of novel targeted therapy. RECENT FINDINGS: Dual human epidermal growth factor 2 (HER2)-targeting concurrent with chemotherapy has demonstrated superiority over chemotherapy with trastuzumab alone. Bevacizumab appears to have a modest effect on pCR rates and its role in neoadjuvant treatment remains uncertain. Despite promising preclinical signals, mTOR inhibition in combination with chemotherapy has yet to yield a benefit in the neoadjuvant setting and trials are ongoing. In contrast, mTOR inhibition in combination with endocrine therapy has demonstrated potential as NAT. SUMMARY: Dual HER2-targeting considerably improves pCR rates. Thus, far incorporation of non-HER2 targeted agents has been less successful. NAT provides an opportunity to evaluate novel agents, and thereby assist the development of a rationale adjuvant strategy, and facilitates the collection of samples for correlative research into breast cancer biology and predictive biomarkers/pathways.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".