Management Strategies for Older Patients with Low-Risk Early-Stage Breast Cancer: A Physician Survey
Bibliographic record
Abstract
When managing older patients with lower-risk hormone-receptor-positive (HR+), HER2 negative (HER2-) early-stage breast cancer (EBC), the harms and benefits of adjuvant therapies should be taken into consideration. A survey was conducted among Canadian oncologists on the definitions of "low risk" and "older", practice patterns, and future trial designs. We contacted 254 physicians and 21% completed the survey (50/242). Most respondents (68%, 34/50) agreed with the definition of "low risk" HR+/HER2- EBC being node-negative and either: ≤3 cm and low histological grade, ≤2 cm and intermediate grade, or ≤1 cm and high grade. The most popular chronological and biological age definition for older patients was ≥70 (45%, 22/49; 45% 21/47). In patients ≥ 70 with low risk EBC, most radiation and medical oncologists would recommend post-lumpectomy radiotherapy (RT) and endocrine therapy (ET). Seventy-eight percent (38/49) felt that trials are needed to evaluate RT and ET's role in patients ≥ 70. The favored design was ET alone, vs. RT plus ET (39%, 15/38). The preferred primary and secondary endpoints were disease-free survival and quality of life, respectively. Although oncologists recommended both RT and ET, there is interest in performing de-escalation trials in patients ≥ 70.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".