Impact and Feasibility of a Comprehensive Geriatric Assessment in the Oncology Setting
Bibliographic record
Abstract
OBJECTIVES: A comprehensive geriatric assessment (CGA) is an objective means of assessing the global health of older patients. While evidence suggesting its promise in improving outcome prediction in the oncology setting is growing, its benefit in guiding treatment decisions remains uncertain. We sought to determine the feasibility and impact of CGA, from a consultative geriatric-oncology service, on treatment decisions in older cancer patients. METHODS: A pilot clinic, where patients underwent CGA with a medical oncologist and geriatrician, was established. Patients ≥70 years, with gastrointestinal or lung cancer were eligible. Following standard assessment by the primary oncologist, a treatment decision was recorded. Patients subsequently underwent a CGA. The final treatment plan was made by the primary oncologist after receipt of findings and recommendations from the CGA. Changes in treatment decisions were recorded. RESULTS: The study enrolled from January to October 2009. Of 168 eligible patients, 120 (71%) were not referred for assessment. Thirty of 48 patients approached underwent CGA. In six patients the treatment plan was undecided at time of referral. In five of these, CGA impacted the ultimate decision (83%). Where the management plan was decided at time of referral (n=24), CGA impacted the final decision in only 1 patient (4%). Previously unidentified medical problems were identified in 70% of patients. CONCLUSIONS: Several factors limited the feasibility of a consultation-type geriatric-oncology service to assess older cancer patients. The impact of CGA in informing treatment decisions was modest but may be of value when the initial treatment decision is uncertain.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".