Challenges Facing Radiation Oncologists in The Management of Older Cancer Patients: Consensus of The International Geriatric Radiotherapy Group
Bibliographic record
Abstract
The management of older cancer patients remains difficult because of data paucity. Radiation oncologists need to identify potential issues which could affect treatment of those patients. A workshop was organized in Barcelona among international radiation oncologists with special interest in the management of older cancer patients on April 22, 2018. The following consensus was reached: 1. Older cancer patients often faced unconscious discriminating bias from cancer specialists and institutions because of their chronological age. 2. Advances in radiotherapy techniques have allowed patients with multiple co-morbidities precluding surgery or systemic therapy to achieve potential cure in early disease stages. 3. The lack of biomarkers for frailty remains an impediment to future research. 4. Access to healthcare insurance and daily transportation remains an issue in many countries; 5. Hypofractionation, brachytherapy, or stereotactic techniques may be ideally suited for older cancer patients to minimize transportation issues and to improve tolerance to radiotherapy. 6. Patients with locally advanced disease who are mentally and physically fit should receive combined therapy for potential cure. 7. The role of systemic therapy alone or combined with radiotherapy for frail patients needs to be defined in future clinical trials because of targeted agents or immunotherapy may be less toxic compared to conventional chemotherapy.
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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 | 0.040 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 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".