Colorectal Cancer Patients’ Reported Frequency, Content, and Satisfaction with Advance Care Planning Discussions
Bibliographic record
Abstract
(1) Background: This observational cohort study describes the frequency, content, and satisfaction with advance care planning (ACP) conversations with healthcare providers (HCPs), as reported by patients with advanced colorectal cancer. (2) Methods: The patients were recruited from two tertiary cancer centers in Alberta, Canada. Using the My Conversations survey with previously validated questions, the patients were asked about specific ACP elements discussed, with which HCPs these elements were discussed, their satisfaction with these conversations, and whether they had a goals of care designation (GCD) order. We surveyed and analyzed data from the following four time points: enrollment, months 1, 2, and 3. (3) Results: In total, 131 patients were recruited. At enrollment, 24% of patients reported discussing at least one ACP topic. From enrollment to month 3, patients reported a high frequency of discussions (80.2% discussed fears, 71.0% discussed prognosis, 54.2% discussed treatment preferences at least once); however, only 44.3% of patients reported discussing what is important to them in considering health care preferences. Patients reported having ACP conversations most often with their oncologists (84.7%) and cancer clinic nurses (61.8%). Patients reported a high level of satisfaction with their ACP conversations, with over 80% of patients reported feeling heard and understood. From enrollment to month 3, there was an increase in the number of patients with a GCD order from 53% to 74%. (4) Conclusions: Patients reported more frequent conversations compared to the literature and clinical documentation. While the satisfaction with these conversations is high, there is room for quality improvement, particularly in eliciting patients’ personal goals for their treatment.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".