MétaCan
Menu
Back to cohort
Record W2583605947 · doi:10.1111/jgs.14713

Depression Case Finding in Individuals with Dementia: A Systematic Review and Meta‐Analysis

2017· review· en· W2583605947 on OpenAlexafffund
Zahra Goodarzi, Bria Mele, Derek J. Roberts, Jayna Holroyd‐Leduc

Bibliographic record

VenueJournal of the American Geriatrics Society · 2017
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsQuest University CanadaUniversity of CalgaryAlberta Health Services
FundersCanadian Institutes of Health ResearchAlberta Innovates - Health Solutions
KeywordsDementiaMedicineGeriatric Depression ScaleDepression (economics)Meta-analysisRating scalePsychiatryClinical Dementia RatingGerontologyInternal medicinePsychologyDiseaseDepressive symptomsCognition

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.056
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.032
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.082
GPT teacher head0.415
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations68
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the American Geriatrics SocietySame topicDementia and Cognitive Impairment ResearchFrench-language works237,207