Implementing geriatric assessment and management for older Canadians with cancer: Adherence to and satisfaction with the intervention, results of the 5C study
Bibliographic record
Abstract
INTRODUCTION: Geriatric assessment and management (GAM) is recommended by professional organizations and recently several randomized controlled trials (RCTs) demonstrated benefits in multiple health outcomes. GAM typically leads to one or more recommendations for the older adult on how to optimize their health. However, little is known about how well recommendations are adhered to. Understanding these issues is vital to designing GAM trials and clinical programs. Therefore, the aim of this study was to examine the number of GAM recommendations made and adherence to and satisfaction with the intervention in a multicentre RCT of GAM for older adults with cancer. MATERIALS AND METHODS: The 5C study was a two-group parallel RCT conducted in eight hospitals across Canada. Each centre kept a detailed recruitment and retention log. The intervention teams documented adherence to their recommendations. Medical records were also reviewed to assess which recommendations were adhered to. Twenty-three semi-structured interviews were conducted with 12 members of the intervention teams and 11 oncology team members to assess implementation of the study and the intervention. RESULTS: Of the 350 participants who were enrolled, 173 were randomized to the intervention arm. Median number of recommendations was seven. Mean adherence to recommendations based on the GAM was 69%, but it varied by type of recommendation, ranging from 98% for laboratory tests to 28% for psychosocial/psychiatry oncology referrals. There was no difference in the number of recommendations or non-adherence to recommendations by sex, level of frailty, or functional status. Oncologists and intervention team members were satisfied with the study implementation and intervention delivery. DISCUSSION: Adherence to recommendations was variable. Adherence to laboratory investigations and further imaging were generally high but much lower for recommendations regarding psychosocial support. Further collaborative work with older adults with cancer is needed to understand how to optimize the intervention to be consistent with patient goals, priorities, and values to ensure maximal impact on health outcomes.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".