Updated recommendations of the International Society of Geriatric Oncology on prostate cancer management in older patients
Bibliographic record
Abstract
BACKGROUND: The median age of prostate cancer diagnosis is 66 years, and the median age of men who die of the disease is eighty years. The public health impact of prostate cancer is already substantial and, given the rapidly ageing world population, can only increase. In this context, the International Society of Geriatric Oncology (SIOG) Task Forces have, since 2010, been developing guidelines for the management of senior adults with prostate cancer. MATERIAL AND METHODS: Since prostate cancer and geriatric oncology are both rapidly evolving fields, a new multidisciplinary Task Force was formed in 2018 to update SIOG recommendations, principally on health status screening tools and treatment. The task force reviewed pertinent articles published between June 2016 and June 2018 and abstracts from European Association of Urology (EAU), European Society for Medical Oncology (ESMO), American Society of Clinical Oncology (ASCO) and American Society of Clinical Oncology Genito-urinary (ASCO GU) meetings over the same period, using search terms relevant to prostate cancer, the elderly, geriatric evaluation, local treatments and advanced disease. Each member of the group proposed modifications to the previous guidelines. These were collated and circulated. The final manuscript reflects the expert consensus. RESULTS: The 2019 consensus is that men aged 75 years and older with prostate cancer should be managed according to their individual health status, and not according to age. Based on available rapid health screening tools, geriatric evaluation and geriatric interventions, the Task Force recommends that patients are classified according to health status into three groups: (1) 'healthy' or 'fit' patients should have the same treatment options as younger patients; (2) 'vulnerable' patients are candidates for geriatric interventions which-if successful-may make it appropriate for them to receive standard treatment and (3) 'frail' patients with major impairments who should receive adapted or palliative treatment. The 2019 SIOG Task Force recommendations also discuss prospects and unmet needs for health status evaluation in everyday practice in older patients with prostate cancer.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".