What is the role of comorbidity, frailty, and functional status in the decision-making process for older adults with cancer and their family members, oncologists, and family physician?
Bibliographic record
Abstract
92 Background: Little is known about the treatment decision making process (TDMP) in older adults (OA) with cancer. The objective of this study is to develop a theoretical framework with the aim to improve the TDMP for this population. Methods: This is a mixed methods multi-perspective longitudinal study. OAs aged > 70 years with advanced prostate, breast, colorectal, or lung cancer, their family members, oncologists and family physicians are invited to participate in individual, semi-structured interviews. Each OA also completes a short survey to characterize their health, functional status, frailty level, decision-making preferences, and satisfaction with the TDMP. The sample is stratified on age (70-79 and 80+) to obtain data saturation for the oldest old. All interviews will be analyzed using the grounded-theory approach. Results: To date, 32 first interviews and 15 second interviews have been completed with 32 older adults, 21 family members and 12 family physicians and 7 cancer specialists. Interviews lasted between 10-60 minutes. Most older adults felt that they should have the final say in the treatment decision, but strongly valued their physician’s opinion. Most participants felt they received enough information, time and support from the oncologist to make their decision. About half the participants went to see their family physician to talk about the diagnosis and plan. Comorbidity and potential side-effects did not play a major role in the decision-making processes for patients and families but it did for oncologists. Family physicians reported they were not involved in treatment decisions, and they preferred more timely information about the patient. Conclusions: This study-in-progress is examining the TDMP from four different perspectives and examining changes over time in the TDMP. Patients and family members were generally satisfied with the treatment decision making process. Final results will be presented at the conference.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| 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".