A Survey of Delirium Self-Reported Knowledge and Practices among Emergency Physicians in the United States
Bibliographic record
Abstract
Objective: This study aimed to evaluate United States emergency physicians’ self-reported knowledge and practices regarding the detection, prevention, and management of delirium, a common and deadly syndrome that disproportionately affects older emergency department (ED) patients. Knowledge and practices of the broader emergency physician community about these priority topics in geriatric emergency medicine are understudied. Design: Electronic self-administered online survey Setting: United States Participants: One-hundred ninety-seven emergency physicians of the American College of Emergency Physicians Emergency Medicine Practice Research Network Measures: Descriptive statistics were generated from survey responses. Results: Of 734 physicians in the research network who were sent the survey, 197 (27%) responded. Most respondents reported intermediate (46%) or advanced (46%) knowledge of delirium detection and management and intermediate (61%) or advanced (21%) knowledge of delirium prevention. Forty percent reported low concern or neutrality over discharging a patient with delirium from the ED. There was high variability in respondents’ perception about the prioritization of delirium in their EDs, and only 14% reported the ED where they worked had a protocol addressing delirium. Participants identified multiple challenges in diagnosing, preventing, and managing delirium, including the physical space and logistics of the emergency care environment (82%), challenges identifying delirium in patients with dementia (75%), and time constraints (64%). Most (69%) perceived utility in increased clinician education on delirium. Conclusions: Surveyed emergency physicians self-report a high knowledge of delirium detection and management, in contrast to prior research demonstrating low ED delirium detection rates. The variable institutional prioritization of delirium reported also does not align with that of geriatric emergency medicine experts and associations, suggesting a need for strategies to bridge this gap.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".