Patient–Physician Discordance in Goals of Care for Patients with Advanced Cancer
Bibliographic record
Abstract
Background: Shared decision-making at end of life (EOL) requires discussions about goals of care and prioritization of length of life compared with quality of life. The purpose of the present study was to describe patient and oncologist discordance with respect to goals of care and to explore possible predictors of discordance. Methods: Patients with metastatic cancer and their oncologists completed an interview at study enrolment and every 3 months thereafter until the death of the patient or the end of the study period (15 months). All interviewees used a 100-point visual analog scale to represent their current goals of care, with quality of life (scored as 0) and survival (scored as 100) serving as anchors. Discordance was defined as an absolute difference between patient and oncologist goals of care of 40 points or more. Results: The study enrolled 378 patients and 11 oncologists. At baseline, 24% discordance was observed, and for patients who survived, discordance was 24% at their last interview. For patients who died, discordance was 28% at the last interview before death, with discordance having been 70% at enrolment. Dissatisfaction with EOL care was reported by 23% of the caregivers for patients with discordance at baseline and by 8% of the caregivers for patients who had no discordance (p = 0.049; φ = 0.20). Conclusions: The data indicate the presence of significant ongoing oncologist–patient discordance with respect to goals of care. Early use of a simple visual analog scale to assess goals of care can inform the oncologist about the patient’s goals and lead to delivery of care that is aligned with patient goals.
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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 | 0.008 | 0.039 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".