Depression Case Finding in Individuals with Dementia: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVES: To compare the diagnostic accuracy of depression case finding tools with a criterion standard in the outpatient setting among adults with dementia. DESIGN: Systematic review and meta-analysis. SETTING: Studies of older outpatients with dementia. PARTICIPANTS: Elderly outpatients (clinic and long-term care) with dementia (N = 3,035). MEASUREMENTS: Prevalence of major depression and diagnostic accuracy measures including sensitivity, specificity, and likelihood ratios. RESULTS: From the 11,539 citations, 20 studies were included for qualitative synthesis and 15 for a meta-analysis. Tools included were the Montgomery Åsberg Depression Rating Scale, Cornell Scale for Depression in Dementia (CSDD), Geriatric Depression Scale (GDS), Center for Epidemiologic Studies Depression Scale (CES-D), Hamilton Depression Rating Scale (HDRS), Single Question, Nijmegen Observer-Rated Depression Scale, and Even Briefer Assessment Scale-Depression. The pooled prevalence of depression in individuals with dementia was 30.3% (95% CI = 22.1-38.5). The average age was 75.2 (95% CI = 71.7-78.7), and mean Mini-Mental State Examination scores ranged from 11.2 to 24. The diagnostic accuracy of the individual tools was pooled for the best-reported cutoffs and for each cutoff, if available. The CSDD had a sensitivity of 0.84 (95% CI = 0.73-0.91) and a specificity of 0.80 (95% CI = 0.65-0.90), the 30-item GDS (GDS-30) had a sensitivity of 0.62 (95% CI = 0.45-0.76) and a specificity 0.81 (95% CI = 0.75-0.85), and the HDRS had a sensitivity of 0.86 (95% CI = 0.63-0.96) and a specificity of 0.84 (95% CI = 0.76-0.90). Summary statistics for all tools across best-reported cutoffs had significant heterogeneity. CONCLUSION: There are many validated tools for the detection of depression in individuals with dementia. Tools that incorporate a physician interview with patient and collateral histories, the CSDD and HDRS, have higher sensitivities, which would ensure fewer false-negatives.
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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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".