4 AT Delirium Assessment Tool in Hospitalized Non-ICU Patients (≥65 Years): A Systematic Review on Validity and Reliability
Bibliographic record
Abstract
Background: Delirium is a frequent acute neuropsychiatric illness that affects attention, consciousness, and cognition. Objectives: The 4AT evaluation tool's validity and reliability in hospitalized non-ICU patients over 65 were assessed in this systematic study. Method: PRISMA guidelines and the PICO framework were used, and relevant research papers were found utilizing several databases (PubMed, Scopus, Web of Sciences, and ScienceDirect). The Mixed Methods Appraisal Tool was used to evaluate the study's quality. Results: 257 relevant publications were found, and only ten articles were selected based on inclusion criteria after the screening. Several studies were reported from various regions, including Asia, Europe, Canada, and Australia. Furthermore, studies found varying prevalence levels for 4AT and control groups, with the greatest for the 4AT group being 40.32%. Moreover, most research employed DSM-5 criteria, while some relied on CAM, DSM-4, and Psychiatric examination by qualified clinicians. Meanwhile, the sensitivity varied from 70% to 100%, and the specificity ranged from 71.6% to 99.2%. In contrast, other assessment tools, such as CAM and OBS, also demonstrated sensitivity and specificity. The main advantage was the time to complete the 4AT tool, which required 2-3 minutes, whereas the other tools took 3.6 and 12.46 minutes, respectively. The 4AT tool was a rapid, validated, easy patient assessment tool. In addition, it was found to improve delirium diagnosis. Conclusion: The tool has been found to have good sensitivity and specificity, and it may be completed quickly by non-specialists.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".