Impact of a geriatric oncology (GO) clinic on cancer treatment plans and supportive care for older adults with genitourinary (GU) cancer.
Bibliographic record
Abstract
238 Background: Older adults with cancer are complex due to multiple comorbidities, polypharmacy, and functional/cognitive impairments, leading to over-treatment and undertreatment. Studies examining the impact of geriatric assessment (GA) in older adults with various cancers found a median of 39% of treatment plans were revised after the GA ( Ann Oncol 2014; 25:307). Although these findings support the need for a GO clinic, whether such a clinic leads to changes in treatment in patients with GU malignancies is not clear. We examined the impact on the treatment plan of a GO assessment in GU patients, along with other enhancements to patient care for consecutive patients referred to the GO clinic in a tertiary care hospital. Methods: All referred older adults (age 65+) with a GU malignancy seen in the GO clinic at the Princess Margaret Cancer Centre, Toronto, Canada between July 2015 and June 2017 were included. Patients were seen by a geriatric oncologist and GO nurse and treatment recommendations were provided to the referring oncologist. Pre- and post-GO clinic treatment plans and enhancements to supportive care were recorded prospectively. Analyses were descriptive. Results: 98 patients (mean age 80) were seen in the GO clinic, of whom 35 were seen pre-treatment (25 prostate, 5 kidney, 5 bladder). 31 of 35 were referred for input on the treatment plan. The initial treatment plan included surgery (n = 6), radiation (n = 14), systemic therapy (n = 8), and hormonal therapy (n = 11). The final treatment plan was intensified in 1 patient, reduced in 6 patients, changed to best supportive care in 8 patients, and unchanged in 15 patients. Overall, GO resulted in a treatment modification in 52% of patients. Enhancements to care included comorbidity management (87%), educational support (94%), disease-related symptoms (39%), and peri-operative management (19%). Conclusions: The GO clinic results in modifications to the proposed treatment plan in over half of referred patients (predominantly a reduction in treatment intensity), and enhancements to care in the vast majority of patients. GO clinics have the potential to avoid overtreatment and improve care for older adults with GU malignancies.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".