GRADE‐Based Clinical Practice Guidelines for Emergency Department Delirium Risk Stratification, Screening, and Brain Imaging in Older Patients With Suspected Delirium
Bibliographic record
Abstract
OBJECTIVES: This portion of the Geriatric Emergency Department (GED) Guidelines 2.0 focuses on delirium in the emergency department (ED). METHODS: A multidisciplinary group applied the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of evidence and develop recommendations related to older ED patients with possible delirium. RESULTS: The GED Guidelines 2.0 Delirium Work Group derived six evidence-based recommendations for risk stratification, diagnosis, and brain imaging. To reduce universal screening, the Delirium Risk Score may be used to identify older adults at low risk for delirium, though the evidence certainty is very low. In adults over 65 admitted to ED observation units, Zucchelli's risk assessment tool (threshold ≥ 4) may stratify delirium risk, also with very low certainty. For adults over 75, the REDEEM Score may be used to identify low- or high-risk individuals, again with very low certainty. For diagnosis, 4AT, bCAM, CAM-ICU, mCAM, AMT-4, or RASS may be used to rule delirium in or out, based on very low certainty. The Delirium Triage Screen (DTS) may be used to rule out, but not to rule in, delirium, also with very low certainty. For diagnostic imaging, there is very low certainty of evidence to recommend for or against obtaining a head CT as part of the evaluation for older ED patients with delirium. All recommendations are conditional, reflecting very low certainty of evidence due to the lack of high-quality ED-based studies and comparative effectiveness research. CONCLUSION: Rigorous ED-based research is needed to strengthen evidence and guide delirium care for older adults in geriatric emergency medicine.
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 | 0.026 | 0.141 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".