Expert consensus panel guidelines on geriatric assessment in oncology
Bibliographic record
Abstract
Despite consensus guidelines on best practice in the care of older patients with cancer, geriatric assessment (GA) has yet to be optimally integrated into the field of oncology in most countries. There is a relative lack of consensus in the published literature as to the best approach to take, and there is a degree of uncertainty as to how integration of geriatric medicine principles might optimally predict patient outcomes. The aim of the current study was to obtain consensus on GA in oncology to inform the implementation of a geriatric oncology programme. A four-round Delphi process was employed. The Delphi method is a structured group facilitation process, using multiple iterations to gain consensus on a given topic. Consensus was reached on the optimal assessment method and interventions required for the commonly employed domains of GA. Other aspects of GA, such as screening methods and age cut-off for assessment, represented a higher degree of disagreement. The expert panel employed in this study clearly identified the criteria that should be included in a clinical geriatric oncology programme. In the absence of evidence-based guidelines, this may prove useful in the care of older cancer patients.
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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 | 0.124 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".