Breast specimen handling and reporting in the post-neoadjuvant setting: challenges and advances
Bibliographic record
Abstract
Neoadjuvant systemic therapy is becoming more commonly used in patients with earlier stages of breast cancer. To assess tumour response to neoadjuvant chemotherapy, pathological evaluation is the gold standard. Depending on the treatment response, the pathological examination of these specimens can be quite challenging. However, a uniform approach to evaluate post-neoadjuvant-treated breast specimens has been lacking. Furthermore, there is no single universally accepted or endorsed classification system for assessing treatment response in this setting. Recent initiatives have attempted to create a standardised protocol for evaluation of post-neoadjuvant breast specimens. This review outlines the necessary information that should be collected prior to macroscopic examination of these specimens, the recommended and most pragmatic approach to tissue sampling for microscopic examination, describes the macroscopic and microscopic features of post-therapy breast specimens, summarises two commonly used systems for classifying treatment response and outlines the critical variables that should be included in the final pathology report.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".