End-of-Life Care Preferences of Older Patients with Multimorbidity: A Mixed Methods Systematic Review
Bibliographic record
Abstract
Unpredictable disease trajectories make early clarification of end-of-life (EoL) care preferences in older patients with multimorbidity advisable. This mixed methods systematic review synthesizes studies and assesses such preferences. Two independent reviewers screened title/abstracts/full texts in seven databases, extracted data and used the Mixed Methods Appraisal Tool to assess risk of bias (RoB). We synthesized findings from 22 studies (3243 patients) narratively and, where possible, quantitatively. Nineteen studies assessed willingness to receive life-sustaining treatments (LSTs), six, the preferred place of care, and eight, preferences regarding shared decision-making processes. When unspecified, 21% of patients in four studies preferred any LST option. In three studies, fewer patients chose LST when faced with death and deteriorating health, and more when treatment promised life extension. In 13 studies, 67% and 48% of patients respectively were willing to receive cardiopulmonary resuscitation and mechanical ventilation, but willingness decreased with deteriorating health. Further, 52% of patients from three studies wished to die at home. Seven studies showed that unless incapacitated, most patients prefer to decide on their EoL care themselves. High non-response rates meant RoB was high in most studies. Knowledge of EoL care preferences of older patients with multimorbidity increases the chance such care will be provided.
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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 | 0.025 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".