Challenges in Geriatric Oncology—A Surgeon’s Perspective
Bibliographic record
Abstract
As our global population ages, we will see more cancer diagnoses in older adults. Surgery is an important treatment modality for solid tumours, forming the majority of all cancers. However, the management of older adults with cancer can be more complex compared to their younger counterparts. This narrative review will outline the current challenges facing older adults with cancer and potential solutions. The challenges facing older adults with cancer are complex and include lack of high-level clinical trials targeting older adults and selection of the right patient for surgery. This may be standard surgical treatment, minimally invasive surgery or alternative therapies (no surgery) which can be local or systemic. The next challenge is to identify the individual patient's vulnerabilities to allow them to be maximally optimised for treatment. Prehabilitation has been shown to be of benefit in some cancer settings but uniform guidance across all surgical specialties is required. Greater awareness of geriatric conditions amongst surgical oncologists and integration of geriatric assessment into a surgical clinic are potential solutions. Enhanced recovery programmes tailored to older adults could reduce postoperative functional decline. Ultimately, the greatest challenge an older adult with cancer may face is the mindset of their treating clinicians-a shared care approach between surgical oncologists and geriatricians is required.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".