Leave no one behind: A global survey of the current state of geriatric oncology practice by SIOG national representatives
Bibliographic record
Abstract
INTRODUCTION: The Sustainable Development Goals of the United Nations include a commitment to "leave no one behind" as a universal goal. To achieve this in geriatric oncology (GO) worldwide, it is important to understand the current state of GO at an international level. The International Society of Geriatric Oncology (SIOG) has several National Representatives (NRs) who act as SIOG's delegates in their respective countries. The NRs took part in this international survey exploring the state of GO practice, identifying barriers and solutions. MATERIALS AND METHODS: The NRs answered open-ended questions by email from February 2020 to October 2022. The questionnaire domains included the demographic information of older adults for their countries, and the NRs' opinions on whether GO is developing, what the barriers are to developing GO, and proposed actions to remove these barriers. The demographic data of each country reported in the survey was adjusted using literature and database searches. RESULTS: Twenty-one of thirty countries with NRs (70%) participated in this questionnaire study: 12 European, four Asian, two North American, two South American, and one Oceanian. The proportion of the population aged ≥75 years varied from 2.2% to 15.8%, and the average life expectancy also varied from 70 years to 86 years. All NRs reported that GO was developing in their country; four NRs (18%) reported that GO was well developed. Although all NRs agreed that geriatric assessment was useful, only three reported that it was used day-to-day in their countries' clinical practice (14%). The major barriers identified were the lack of (i) evidence to support GO use, (ii) awareness and interest in GO, and (iii) resources (time, manpower, and funding). The major proposed actions were to (i) provide new evidence through clinical trials specific for GO patients, (ii) stimulate awareness through networking, and (iii) deliver educational materials and information to healthcare providers and medical students. DISCUSSION: This current survey has identified the barriers to GO and proposed actions that could remove them. Broader awareness seems to be essential to implementing GO. Additional actions are needed to develop GO within countries and can be supported through international partnerships.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".