Toronto Workshop on Late Recurrence in Estrogen Receptor–Positive Breast Cancer: Part 1: Late Recurrence: Current Understanding, Clinical Considerations
Bibliographic record
Abstract
Disease recurrence (locoregional, distant) exerts a significant clinical impact on the survival of estrogen receptor-positive breast cancer patients. Many of these recurrences occur late, more than 5 years after original diagnosis, and represent a major obstacle to the effective treatment of this disease. Indeed, methods to identify patients at risk of late recurrence and therapeutic strategies designed to avert or treat these recurrences are lacking. Therefore, an international workshop was convened in Toronto, Canada, in February 2018 to review the current understanding of late recurrence and to identify critical issues that require future study. In this article, the major issues surrounding late recurrence are defined and current approaches that may be applicable to this challenge are discussed. Specifically, diagnostic tests with potential utility in late-recurrence prediction are described as well as a variety of patient-related factors that may influence recurrence risk. Clinical and therapeutic approaches are also reviewed, with a focus on patient surveillance and the implementation of extended endocrine therapy in the context of late-recurrence prevention. Understanding and treating late recurrence in estrogen receptor-positive breast cancer is a major unmet clinical need. A concerted effort of basic and clinical research is required to confront late recurrence and improve disease management and patient survival.
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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 | 0.003 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".