Preface to Second Review Issue on Breast Cancer
Bibliographic record
Abstract
It is almost 2 years since the Journal’s first Review Issue on breast cancer was published. Since that time there have been many advances in the field, and it is our pleasure to bring you the second issue on breast cancer. This time, we used a different format. In the first edition, we included state-of-the-art reviews with accompanying commentaries. In this edition, the focus is on the former type of article only. Some of the topics covered previously are now updated, describing research advances and changes in practice. Examples include endocrine therapy for premenopausal and postmenopausal women; imaging of operable breast cancer; controversies related to sentinel node biopsy; and biomarkers for prognosis and predicting therapy. In addition, here we cover important new topics that were not incorporated into the first edition, including survivorship issues related to premature menopause, fatigue, cognitive disturbances, and follow-up care. Finally, to discuss the evolving and challenging area of preoperative chemotherapy, we capitalized on the opportunity of soliciting manuscripts from authors who participated in a National Cancer Institute–sponsored symposium on preoperative chemotherapy for breast cancer, which occurred in March 2007. The resulting Review Issue is a marvelous compendium of 17 articles on a full range of topics in breast cancer. We are grateful of the work of the authors, and we wish you pleasant reading!
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.092 | 0.068 |
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".