Detection of anxiety symptoms and disorders in older adults: a diagnostic accuracy systematic review
Bibliographic record
Abstract
BACKGROUND: Anxiety symptoms and disorders are common in older adults and often go undetected. A systematic review was completed to identify tools that can be used to detect anxiety symptoms and disorders in community-dwelling older adults. METHODS: MEDLINE, Embase and PsycINFO were searched using the search concepts anxiety, older adults and diagnostic accuracy in March 2023. Included articles assessed anxiety in community-dwelling older adults using an index anxiety tool and a gold standard form of anxiety assessment and reported resulting diagnostic accuracy outcomes. Estimates of pooled diagnostic accuracy outcomes were completed. RESULTS: Twenty-three anxiety tools were identified from the 32 included articles. Pooled diagnostic accuracy outcomes were estimated for the Geriatric Anxiety Inventory (GAI)-20 [n = 3, sensitivity = 0.89, 95% confidence interval (CI) = 0.70-0.97, specificity = 0.80, 95% CI = 0.67-0.89] to detect generalized anxiety disorder (GAD) and for the GAI-20 (n = 3, cut off ≥ 9, sensitivity = 0.74, 95% CI = 0.62-0.83, specificity = 0.96, 95% CI = 0.74-1.00), Beck Anxiety Inventory (n = 3, sensitivity = 0.70, 95% CI = 0.58-0.79, specificity = 0.60, 95% CI = 0.51-0.68) and Hospital Anxiety and Depression Scale (HADS-A) (n = 3, sensitivity = 0.78, 95% CI = 0.60-0.89, specificity = 0.76, 95% CI = 0.60-0.87) to detect anxiety disorders in clinical samples. CONCLUSION: The GAI-20 was the most studied tool and had adequate sensitivity while maintaining acceptable specificity when identifying GAD and anxiety disorders. The GAI-20, GAI-Short Form and HADS-A tools are supported for use in detecting anxiety in community-dwelling older adults. Brief, self-rated and easy-to-use tools may be the best options for anxiety detection in community-dwelling older adults given resource limitations. Clinicians may consider factors including patient comorbidities and anxiety prevalence when selecting a tool and cut off.
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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 | 0.001 |
| 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".