Implementation of an Oncogeriatric Unit for Frail Older Patients with Breast Cancer: Preliminary Results
Bibliographic record
Abstract
(1) Background: Breast cancer (BC) has a high incidence in Europe, particularly in older adults. Traditionally under-represented in clinical trials, this age group is often undertreated due to ageism. This study aims to characterize frail older adults (≥70 years) with BC based on a comprehensive geriatric assessment, to guide individualized treatment decision-making. (2) Methods: A descriptive analysis of older adults with BC treated from January 2021 to December 2022 was performed. Data were analyzed based on anonymized electronic medical records. (3) Results: Of 123 patients (mean age 84.0 ± 5.6 years), 122 (99.2%) were women. The mean G8 screening score was 12.1 ± 2.5. Most had functional dependence (69.9% Barthel Index, 81.3% Lawton/Brody Scale) and a moderate-to-high risk of falling (76.4% Tinetti index). Cognitive impairment and malnutrition risk were present in 15.4% and 30.1%, respectively. Prehabilitation inclusive strategies led to adapted treatment in 55.3% of cases. Endocrine therapy, surgery, radiotherapy, and chemotherapy was used in 99.2%, 56.1%, 35.0%, and 8.9% of patients, respectively. (4) Conclusions: Our comprehensive oncogeriatric strategy promotes personalized oncologic treatment, improves outcomes by addressing frailty, and enhances treatment tolerability in older patients with BC, validating the expansion of this combined team approach to other cancer types and institutions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".