A Scoping Review to Map Empirical Evidence Regarding Key Domains and Questions in the Clinical Pathway of Delirium in Palliative Care
Bibliographic record
Abstract
CONTEXT: Based on the clinical care pathway of delirium in palliative care (PC), a published analytic framework (AF) formulated research questions in key domains and recommended a scoping review to identify evidence gaps. OBJECTIVES: To produce a literature map for key domains of the published AF: screening, prognosis and diagnosis, management, and the health-related outcomes. METHODS: A standard scoping review framework was used by an interdisciplinary study team of nurse- and physician-delirium researchers, an information specialist, and review methodologists to conduct the review. Knowledge user engagement provided context in refining 19 AF questions. A peer-reviewed search strategy identified citations in Medline, PsycINFO, Embase, and CINAHL databases between 1980 and 2018. Two reviewers independently screened records for inclusion using explicit study eligibility criteria for the population, design, delirium diagnosis, and investigational intent. RESULTS: Of 104 studies reporting empirical data and meeting eligibility criteria, most were conducted in patients with cancer (73.1%) and in inpatient PC units (52%). The most frequent study design was a one or more group, nonrandomized trial or cohort (67.3%). Evidence gaps were identified: delirium risk prediction; comparative effectiveness and harms of prevention, variability in delirium management across PC settings, advanced directive and substitute decision-maker input, and transition of care location; and estimating delirium reversibility. Future rigorous primary studies are required to address these gaps and preliminary concerns regarding the quality of extant literature. CONCLUSION: Substantial evidence gaps exist, providing opportunities for future research regarding the assessment, prognosis, and management of delirium in PC 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 | 0.016 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".