Diagnostic accuracy of the 4AT for delirium detection in older adults: systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Detection of delirium in hospitalised older adults is recommended in national and international guidelines. The 4 'A's Test (4AT) is a short (<2 minutes) instrument for delirium detection that is used internationally as a standard tool in clinical practice. We performed a systematic review and meta-analysis of diagnostic test accuracy of the 4AT for delirium detection. METHODS: We searched MEDLINE, EMBASE, PsycINFO, CINAHL, clinicaltrials.gov and the Cochrane Central Register of Controlled Trials, from 2011 (year of 4AT release on the website www.the4AT.com) until 21 December 2019. Inclusion criteria were: older adults (≥65 years); diagnostic accuracy study of the 4AT index test when compared to delirium reference standard (standard diagnostic criteria or validated tool). Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Pooled estimates of sensitivity and specificity were generated from a bivariate random effects model. RESULTS: Seventeen studies (3,702 observations) were included. Settings were acute medicine, surgery, a care home and the emergency department. Three studies assessed performance of the 4AT in stroke. The overall prevalence of delirium was 24.2% (95% CI 17.8-32.1%; range 10.5-61.9%). The pooled sensitivity was 0.88 (95% CI 0.80-0.93) and the pooled specificity was 0.88 (95% CI 0.82-0.92). Excluding the stroke studies, the pooled sensitivity was 0.86 (95% CI 0.77-0.92) and the pooled specificity was 0.89 (95% CI 0.83-0.93). The methodological quality of studies varied but was moderate to good overall. CONCLUSIONS: The 4AT shows good diagnostic test accuracy for delirium in the 17 available studies. These findings support its use in routine clinical practice in delirium detection. PROSPERO REGISTRATION NUMBER: CRD42019133702.
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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 | 0.022 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.044 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".