Knowledge and practice of geriatric oncology among cancer care providers in Africa: A CROSS-sectional survey.
Bibliographic record
Abstract
e13848 Background: Africa is witnessing an increase in its older population and in cancer incidence, but little is known about the regional status of geriatric oncology. We conducted a cross-sectional survey among African physicians managing older adults with cancer to understand African geriatric oncology practices. Methods: A 30-item survey on the utilization of geriatric assessment (GA) and on the management of older adults with cancer, based on previously published surveys from the United States and Latin America, was distributed electronically via snowball sampling to physicians practicing oncology in Africa. Answers were collected between April and November 2024. Descriptive statistics were utilized to describe participant answers using SPSS software. Results: 179 participants started the survey and 84 completed it. Participants worked in 14 countries across 5 African regions, and most (76%) were aged < 45Seven percent of participants were surgeons, 13% medical oncologists, 61% clinical/radiation oncologists, and 13% trainees. Seventy-one percent knew the correct definition of older age. Most participants (88%) worked in academic and public institutions, with older adults accounting for 10-40% of treated patients. Most agreed (82%) that older patients should be treated differently, and 92% stated they assessed them differently, but < 20% did so using validated GA tools. Only 7% had a geriatrician at their institution while 21% knew a geriatrician in their city or healthcare system. Among those with a geriatrician at their institution, 83% involved them in managing patients. All respondents agreed that GA is necessary, but only 58% were aware of international geriatric oncology guidelines. The most evaluated domains included instrumental activities of daily living (41%), comorbidities (31%), toxicity risk (27%), and life expectancy (23.8%). Barriers to GA included lack of geriatricians (92%), lack of services available to perform GA (84%), lack of access to interventions (71%), and lack of reimbursement (39%). Most participants strongly agreed that treatment toxicities (84%), comorbidities (84%), patient prognosis (83%), and nutritional status (86%) strongly mattered in making the treatment decisions, while age (65%), was not considered as important. Only 20% believed there is enough information from clinical trials to treat older adults with cancer. Nearly all (97%) agreed that cancer care for older patients should be improved and were interested in receiving training. Conclusions: Geriatric oncology is an unmet need in Africa, and its implementation is limited by a lack of resources and personnel. Training African healthcare workers in geriatric oncology should be a future priority to increase the capacity to provide care for this growing population.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".