Speakers' Abstracts
Bibliographic record
Abstract
The recognition that early breast cancer is a multitude of diseases each requiring a specific systemic therapy has guided the design of the International Breast Cancer Study Group (IBCSG) randomized clinical trials since 1977.Early studies of the Group evaluated chemotherapy and endocrine therapies in subpopulations defined by menopausal status and risk factors.Subsequent trials focused on the timing and duration of chemoendocrine therapies stratified by the endocrine responsiveness of the disease to define personalized treatment strategies.In addition, intensified chemotherapy was tested for patients at very high risk of relapse.The effectiveness of ovarian function suppression/ablation was evaluated in several trials for premenopausal women and the results informed the designs of SOFT and TEXT, which are anticipated to report at the end of this year.Endocrine therapies for postmenopausal women were investigated in several trials using tamoxifen, toremifen or aromatase inhibitors.A risk-adapted model was developed for a personalized approach to patient care.It indicated that for high-risk, higher proliferating breast cancers the efficacy of an aromatase inhibitor is overwhelming when compared to tamoxifen, while the latter may suffice as adjuvant treatment for patients with lower risk breast cancer.The use of anti-HER2 treatments was studied in three consecutive trials within the Breast International Group (BIG) collaboration.These investigated the effectiveness of adjuvant trastuzumab, its duration, and its combination with other anti-HER2 agents.Results from randomized clinical trials are essential for personalizing adjuvant treatments.To define therapies for individual patients we must conduct randomized trials within specific niche populations and perform appropriate subgroup analyses across multiple trials.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.448 | 0.250 |
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".