Evolution and Predictors of Patient–Caregiver Concordance on States of Life-Sustaining Treatment Preferences over Terminally Ill Cancer Patients' Last Six Months of Life
Bibliographic record
Abstract
BACKGROUND: Patient-caregiver concordance on end-of-life (EOL) care preferences is poor, but changes in this concordance have not been longitudinally explored as patient death approaches, potentially distorting the extent of concordance. Cross-sectional studies cannot disentangle whether the extent of concordance is facilitated or hindered by dyads' specific preferences, prognostic awareness, physical and psychological well-being, and quality of life, or whether these variables were enhanced or worsened by patient-caregiver concordance on EOL care preferences. OBJECTIVE: To examine the evolution of and factors facilitating or hindering patient-caregiver concordance on life-sustaining treatment (LST) preferences over cancer patients' last six months. DESIGN: Longitudinal study design. METHODS/SUBJECTS: Patient-caregiver concordance on LST preference states (patterns) was examined among 215 cancer patient-caregiver dyads in patients' last six months by hidden Markov modeling. Concordance on LST preference states was determined by percent agreement and kappa coefficients. Predictors of concordance on LST preference states were tested by hierarchical generalized linear modeling with logistic regression, with concordance and time-varying, modifiable independent variables arranged in a distinct time sequence. RESULTS: Patient-caregiver concordance on LST preference states was poor and improved only slightly over cancer patients' last six months. Concordance on LST preference states was significantly more likely in patients with greater physical symptom distress. Caregivers were more likely to concur with their relative's LST preference states if caregivers uniformly rejected all LSTs or accepted nutritional support while rejecting other aggressive LSTs for their relative. DISCUSSION/CONCLUSION: Patient symptom distress and caregiver rejection of aggressive LSTs predicted greater patient-caregiver concordance on LST preference states in patients' last six months. To encourage patients and caregivers to discuss LST preferences, clinicians should facilitate caregivers' understanding of patients' LST preferences and LST efficacy at EOL and adjustment to their beloved's inevitable death when his/her physical symptoms still wax and wane, thus providing personalized and value-concordant EOL care for dying cancer patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".