De-Escalation of Treatment in Women Aged ≥80 Years with Breast Cancer: A Retrospective Analysis from Two Breast Centers
Bibliographic record
Abstract
Background: Breast cancer is frequently diagnosed in older women. However, the impact of surgery on survival is not well studied and prognosis for women ≥ 80 years of age is progressively depending on comorbidities. Methods: Medical records of consecutive women aged ≥ 80 years diagnosed with primary breast cancer treated with upfront surgery at two Breast Centers from 2011 to 2021 were retrospectively analyzed. Results: A total of 553 consecutive women with a median age of 83 years and a median tumor diameter of 21 mm were analyzed (574 lesions). Clinical Stages II or III were found in 263/574 (46%) and 101/574 cases (18%), respectively. Axillary staging was completely omitted for 94/542 invasive lesions (17%), and this increased over time from 2% to 33% (p < 0.001). Adjuvant hormone therapy and radiotherapy were omitted in 134/490 (27%) and in 122/420 patients (29%), respectively, while only 26/195 (13%) of patients with a clear clinical indication received adjuvant chemotherapy. At a median follow-up of 61 months (6–147) the 5- and 10-years overall survival (OS) were 64% and 21%, while breast cancer-specific survival (BCSS) at 5 and 10 years were 94% and 78%, respectively. Adjuvant therapies were not associated with a significant improvement in BCSS, while worse OS was associated with older age or more comorbidities as measured by the Charlson Comorbidity Index (CCI) (p < 0.001 and p = 0.012, respectively). Conclusions: Breast surgery, when possible, has a primary role even for women > 80 years of age, and it is associated with a reasonable BCSS. De-escalation of adjuvant therapies should be considered in this setting because survival is largely determined by age and co-morbidities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".