An Analytical Framework for Delirium Research in Palliative Care Settings: Integrated Epidemiologic, Clinician-Researcher, and Knowledge User Perspectives
Bibliographic record
Abstract
CONTEXT: Delirium often presents difficult management challenges in the context of goals of care in palliative care settings. OBJECTIVES: The aim was to formulate an analytical framework for further research on delirium in palliative care settings, prioritize the associated research questions, discuss the inherent methodological challenges associated with relevant studies, and outline the next steps in a program of delirium research. METHODS: We combined multidisciplinary input from delirium researchers and knowledge users at an international delirium study planning meeting, relevant literature searches, focused input of epidemiologic expertise, and a meeting participant and coauthor survey to formulate a conceptual research framework and prioritize research questions. RESULTS: Our proposed framework incorporates three main groups of research questions: the first was predominantly epidemiologic, such as delirium occurrence rates, risk factor evaluation, screening, and diagnosis; the second covers pragmatic management questions; and the third relates to the development of predictive models for delirium outcomes. Based on aggregated survey responses to each research question or domain, the combined modal ratings of "very" or "extremely" important confirmed their priority. CONCLUSION: Using an analytical framework to represent the full clinical care pathway of delirium in palliative care settings, we identified multiple knowledge gaps in relation to the occurrence rates, assessment, management, and outcome prediction of delirium in this population. The knowledge synthesis generated from adequately powered, multicenter studies to answer the framework's research questions will inform decision making and policy development regarding delirium detection and management and thus help to achieve better outcomes for patients in palliative care settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.124 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.011 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| 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".