Diagnostic Accuracy of Tools to Identify Anxiety Symptoms and Disorders in Persons with Dementia: a Systematic Review Update
Bibliographic record
Abstract
Limited anxiety tools validated against a diagnostic gold standard in the context of dementia have been identified. A systematic review update was completed to identify publications since 2017 describing tools to detect anxiety in persons with dementia compared to a diagnostic gold standard. The original search strategy was replicated in MEDLINE, Embase, and PsycINFO. Pooled prevalence and diagnostic accuracy estimates were calculated based on three or more studies presenting comparable information from either the original or updated review search. No pooled estimates were calculated in the original review given limited evidence. One study evaluating the Rating Anxiety in Dementia (RAID) scale, Neuropsychiatric Inventory, and Hamilton Anxiety Rating Scale met review inclusion criteria. The pooled prevalence of generalized anxiety disorder (GAD) (n=3, 36.7%, 95% Confidence Interval (CI): 24.3-49.2%) and diagnostic accuracy outcomes for the RAID scale (≥11 cut-off) to detect GAD (n=3, sensitivity: 89%, 95% CI: 78-95%; specificity: 73%, 95% CI: 60-82%) were calculated using data from two studies identified in the original review and the newly included study from the review update. The RAID scale has the most evidence and adequate sensitivity to support its use in identifying anxiety in persons with dementia. Validated tools to detect anxiety in persons with dementia remain understudied.
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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 | 0.018 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 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".