Pilot randomized trial of a transdisciplinary geriatric intervention for older adults with cancer.
Bibliographic record
Abstract
11549 Background: Oncologists often struggle with managing the unique care needs of older adults with cancer. We sought to determine the feasibility of delivering a transdisciplinary geriatric intervention designed to address the geriatric (physical function & comorbidity) and palliative care (symptoms & prognostic understanding) needs of older adults with cancer. Methods: We randomly assigned patients age ≥65 with newly diagnosed incurable gastrointestinal (GI) or lung cancer to receive a transdisciplinary geriatric intervention or usual care. Intervention patients received two visits with a geriatrician who was trained to address patients’ palliative care needs in addition to conducting a geriatric assessment. We defined the intervention as feasible if > 70% of patients enrolled in the study and > 75% completed study visits and surveys. At baseline and week 12, we assessed patients’ quality of life (QOL, Functional Assessment of Cancer Therapy General), symptoms (Edmonton Symptom Assessment System), and communication confidence (Perceived Efficacy in Patient Physician Interactions). As this was a pilot study, we calculated mean change scores in outcomes and estimated intervention effect sizes (ES). Results: From 2/2017-6/2018, we randomized 62 patients (55.9% enrollment rate [most common reason for refusal was feeling too ill]; median age = 72.3 [range 65.2-91.8]; 45.2% female; cancer types: 56.5% GI, 43.5% lung). Among intervention patients, 82.1% attended the first visit and 76.2% attended both. Overall, 77.8% completed all study surveys. Compared to usual care, intervention patients had less decrement in QOL scores (-0.77 vs -3.84, ES = .21), greater reduction in the number of moderate/severe symptoms (-0.69 vs +1.04, ES = .58), and more improvement in communication confidence (+1.06 vs -0.80, ES = .38). Conclusions: In this trial of older adults with advanced cancer, more than half enrolled in the study and over 75% of those who enrolled completed all study visits and surveys. Our effect size estimates suggest that a transdisciplinary intervention targeting patients’ geriatric and palliative care needs may be a promising approach to improve patients’ QOL, symptom burden, and communication confidence. Clinical trial information: NCT02868112.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".