A world-wide study on delirium assessments and presence of protocols
Bibliographic record
Abstract
BACKGROUND: Delirium is a common complication of older people in hospitals, rehabilitation and long-term facilities. OBJECTIVE: To assess the worldwide use of validated delirium assessment tools and the presence of delirium management protocols. DESIGN: Secondary analysis of a worldwide one-day point prevalence study on World Delirium Awareness Day, 15 March 2023. SETTING: Cross-sectional online survey including hospitals, rehabilitation and long-term facilities. METHODS: Participating clinicians reported data on delirium, the presence of protocols, delirium assessments, delirium-awareness interventions, non-pharmacological and pharmacological interventions, and ward/unit-specific barriers. RESULTS: Data from 44 countries, 1664 wards/units and 36 048 patients were analysed. Validated delirium assessments were used in 66.7% (n = 1110) of wards/units, 18.6% (n = 310) used personal judgement or no assessment, and 10% (n = 166) used other assessment methods. A delirium management protocol was reported in 66.8% (n = 1094) of wards/units. The presence of protocols for delirium management varied across continents, ranging from 21.6% (on 21/97 wards/units) in Africa to 90.4% (235/260) in Australia, similar to the use of validated delirium assessments with 29.6% (29/98) in Africa to 93.5% (116/124) in North America. Wards/units with a delirium management protocol [n = 1094/1664, 66.8%] were more likely to use a validated delirium test than those without a protocol [odds ratio 6.97 (95% confidence interval 5.289-9.185)]. The presence of a delirium protocol increased the chances for valid delirium assessment and, likely, evidence-based interventions. CONCLUSION: Wards/units that reported the presence of delirium management protocols had a higher probability of using validated delirium assessments tools to assess for delirium.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".