Identifying Geriatric Oncology Competencies for Medical Oncology Trainees: A Modified Delphi Consensus Study
Bibliographic record
Abstract
BACKGROUND: Most oncology trainees are not taught about the needs of older patients, who make up the majority of patients with cancer. Training of health care providers is critical to improve the care of older adults with cancer. There is no consensus about which geriatric oncology (GO) competencies are important for medical oncology trainees. Our objective was to identify GO competencies medical oncology trainees should acquire during training. MATERIALS AND METHODS: A modified Delphi consensus of experts in oncology medical education and GO was conducted. Experts categorized at what training stage proposed competencies should be attained: internal medicine, oncology, or GO training. Consensus was obtained if two thirds of experts agreed on the training stage at which the competency should be attained. RESULTS: A total of 78 potential competencies were identified, of which 35 (44.9%) proposed competencies were felt to be appropriate to be acquired during oncology training. The majority of the identified competencies pertained to prescribing of systemic therapy (n = 12) and psychosocial and supportive care (n = 13). No competencies related to geriatric assessment were identified for acquisition during oncology training. CONCLUSION: Experts in oncology education and geriatric oncology agreed upon a set of GO competencies appropriate for oncology trainees. These results provide the foundation for developing a GO curriculum for medical oncology trainees and will hopefully lead to better care of older adults with cancer. IMPLICATIONS FOR PRACTICE: The aging population will drive the projected rise in cancer incidence. Although aging patients make up the majority of patients diagnosed with cancer, oncologists rarely receive training on how to care for them. Training of health care providers is critical to improving the care of older adults with cancer. The results of this study will help form the foundation of developing a geriatric oncology curriculum for medical oncology trainees.
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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 | 0.109 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".