Editorial: Recent advancements in neoadjuvant chemotherapy for specific breast cancer subtypes
Bibliographic record
Abstract
Most locally advanced breast cancer patients undergo pre-surgery treatment known as neoadjuvant chemotherapy (NAC). The purpose of NAC is to reduce the tumor’s size and improve surgical outcomes, cosmetic results, and chances of conservative breast surgery, control tumor progression and observe tumor sensitivity (or resistance) to the chosen treatment regimen (1–4). Several studies have suggested better survival outcomes in patients achieving complete pathological remission than in patients with residual or progressive disease at the time of definitive surgery (5, 6). However, the mechanisms of primary resistance and strategies to overcome those are a matter of intense research. Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype and is responsible for most of the annual mortality rate of breast cancer (7, 8). This Research Topic focused on studies that tackle the most recent advances in treating breast cancer using NAC. Pegylated liposomal doxorubicin (PLD) is used safely to treat breast cancer patients (9). In addition, it has a superior benefit over free doxorubicin since it is distributed in smaller volumes with extended circulation time (10). A recent clinical trial demonstrated that pegylated liposomal doxorubicin (PLD) is safe for TNBC with a particular focus on elderly patients and those with risks of developing cardiotoxicity (Gil-Gil et al.)
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.019 | 0.016 |
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".