World delirium awareness and quality survey in 2023—a worldwide point prevalence study
Bibliographic record
Abstract
BACKGROUND: Delirium, an acute brain dysfunction, is proposed to be highly prevalent in clinical care and shown to significantly increase the risk of mortality and dementia. OBJECTIVES: To report on the global prevalence of clinically documented delirium and delirium-related clinical practices in wards caring for paediatric and adult patients in healthcare facilities. DESIGN: A prospective, cross-sectional, 39-question survey completed on World Delirium Awareness Day, 15 March 2023. PARTICIPANTS: Clinicians or researchers with access to clinical data. MAIN OUTCOME AND MEASURE: The primary outcome was the prevalence of clinically documented delirium at 8:00 a.m. (4 h) and 8:00 p.m. (±4 h). Secondary outcomes included delirium-related care practices and barriers to use. Descriptive statistics were calculated and multilevel modelling was completed. RESULTS: 1664 wards submitted surveys from 44 countries, reporting on delirium assessments at 8:00 a.m. (n = 36 048) and 8:00 p.m. (n = 32 867); 61% reported use of validated delirium assessment tools. At 8:00 a.m., 18% (n = 2788/15 458) and at 8:00 p.m., 17.7% (n = 2454/13 860) were delirium positive. Top prevention measures were pain management (86.7%), mobilisation (81.4%) and adequate fluids (80.4%). Frequently reported pharmacologic interventions were benzodiazepines (52.7%) and haloperidol (46.2%). Top barriers included the shortage of staff (54.3%), lack of time to educate staff (48.6%) and missing knowledge about delirium (38%). CONCLUSION AND RELEVANCE: In this study, approximately one out of five patients were reported as delirious. The reported high use of benzodiazepines needs further evaluation as it is not aligned with best-practice recommendations. Findings provide a benchmark for future quality improvement projects and research.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".