Value of a multidisciplinary geriatric oncology committee on patient care in a community-based, academic cancer center.
Bibliographic record
Abstract
e13771 Background: The heterogeneity in health and functional ability among older patients makes the management of cancer a unique challenge. The Geriatric Oncology Program at the University of Maryland Baltimore Washington Medical Center (BWMC) was created to optimize cancer management and recommendations for older patients. This study aimed to assess the benefits of the implementation of such a program at a community- based academic cancer center. Materials and Methods: We retrospectively analyzed patients aged ≥80 years presenting to the Geriatric Oncology Program between January 2017 and July 2022. A multidisciplinary team of specialists collectively reviewed each patient using geriatric-specific domains and stratified each patient into one of three management groups- Group 1: those deemed fit to receive standard oncologic care (SOC); Group 2: those recommended to receive optimization services prior to reassessment for SOC; and Group 3: those deemed to be best suited for supportive care and/ or hospice care. ANOVA, chi-square, and Kaplan-Meier analyses were used to assess patient outcomes. Results: Among 233 patients included, 76 (32.6%) received SOC (Group 1), 43 (18.5%) were optimized (Group 2), and 114 (49.0%) received supportive care or hospice referral (Group 3). There was no significant difference in sex, race, or age among all three groups. The Canadian Study of Health and Aging-Clinical Frailty Scale (CSHA-CFS) score was implemented in 2019 (n=90). Patients receiving supportive/ hospice care only had an average score of 5.8, while the averages for those in the optimization and SOC groups were 4.6 and 4.1, respectively (p=< .001). SOC patients had the longest average survival of 2.71 years compared to the optimization (2.30 years) and supportive care groups (0.93 years) (p= <0.001). 69.8% of optimized patients were deemed fit for SOC upon re-evaluation following optimization services. For all patients that underwent surgical interventions, post- operatively, 23 patients (85%) were discharged home and 4 (15%) were discharged to a rehab facility. The average survival after surgery for all patients was 3.16 years, while patients who were optimized prior to surgery had an average survival after surgery of 3.21 years. Conclusions: The present study demonstrates the need for specialized consideration of the heterogeneity that cancer diagnoses present in older individuals. The Geriatric Oncology Program at BWMC is able to maximize treatment outcomes for geriatric patients through the provision of SOC therapies and optimization services, while also minimizing unnecessary interventions on an individual patient-centric level.
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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 | 0.014 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".