Intelligent Application of Breast Cancer Trials Data in the Clinic
Bibliographic record
Abstract
This meeting commenced with a talk from Prof Loibl on neoadjuvant and adjuvant strategies for HER2positive (human epidermal growth factor receptor 2-positive) early breast cancer (EBC), which featured a précis on the most pertinent, recent trial data and how these data may shape future treatment decisions in clinical practice. Prof Conte moved the discussion forward by addressing how recent studies may lead towards a new standard of care (SoC) and treatment paradigms in patients with metastatic breast cancer. Prof Schmid gave an overview of potential strategies that could be used to prevent or overcome endocrine therapy resistance in patients with hormone receptor-positive breast cancer. The session was concluded with a presentation on ‘Precision Medicine for Metastatic Breast Cancer’ by Prof Sotiriou, in which he highlighted the potential applications of precision medicine and some of the different approaches that have been used in metastatic breast cancer. Prof Verma, the meeting chair, opened the symposium and facilitated the discussion sessions. The contents of the presentations and discussions are summarised herein.
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 | 0.189 | 0.435 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".