Geriatric assessment for older people with cancer: policy recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most cancers occur in older people and the burden in this age group is increasing. Over the past two decades the evidence on how best to treat this population has increased rapidly. However, implementation of new best practices has been slow and needs involvement of policymakers. This perspective paper explains why older people with cancer have different needs than the wider population. An overview is given of the recommended approach for older people with cancer and its benefits on clinical outcomes and cost-effectiveness. In older patients, the geriatric assessment (GA) is the gold standard to measure level of fitness and to determine treatment tolerability. The GA, with multiple domains of physical health, functional status, psychological health and socio-environmental factors, prevents initiation of inappropriate oncologic treatment and recommends geriatric interventions to optimize the patient's general health and thus resilience for receiving treatments. Multiple studies have proven its benefits such as reduced toxicity, better quality of life, better patient-centred communication and lower healthcare use. Although GA might require investment of time and resources, this is relatively small compared to the improved outcomes, possible cost-savings and compared to the large cost of oncologic treatments as a whole.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it