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Record W4404244662 · doi:10.1002/hsr2.70058

Depression detection in dementia: A diagnostic accuracy systematic review and meta analysis update

2024· review· en· W4404244662 on OpenAlexafffund
Kayla Atchison, Anam Nazir, Pauline Wu, Dallas Seitz, Jennifer Watt, Zahra Goodarzi

Bibliographic record

VenueHealth Science Reports · 2024
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of TorontoUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsMeta-analysisDementiaSystematic reviewDepression (economics)Diagnostic accuracyPsychologyMedicineMEDLINEComputer sciencePolitical scienceInternal medicineDisease

Abstract

fetched live from OpenAlex

Abstract Background Depression is common in persons with dementia and is often under‐detected and under‐treated. It is critical to understand which available tools accurately detect depression in the context of dementia. Methods We updated our systematic review completed in 2015. The search strategy of our original review was replicated in Medline, Embase, and PsycINFO. Studies describing the use of a tool to identify depression in persons with dementia, compared to a criterion standard, and reporting diagnostic accuracy outcomes were included in the review update. Pooled prevalence estimates of major depression and pooled estimates of diagnostic accuracy outcomes (i.e., sensitivity [SN], specificity [SP]) for tools were calculated. Results Three studies were included of the 8980 returned from the database search and were added to the prior 20 articles from the 2015 review. The Cornell Scale for Depression in Dementia (CSDD), Geriatric Depression Scale (GDS)−15 item, Neuropsychiatric Inventory‐Depression items (NPI‐D), and Depression in Old Age Scale (DIA‐S) were evaluated in the three studies. Two new studies were added to the existing pooled prevalence estimate of major depression (29%, 95% confidence interval [CI] = 21.6%–36.5%, n = 17) and pooled diagnostic accuracy estimate for the CSDD at the best cut‐off (SN = 0.83, 95% CI = 0.74–0.90; SP = 0.81, 95% CI = 0.69–0.89). New pooled diagnostic accuracy estimates were completed for the CSDD (cut‐off ≥12) (SN = 0.61, 95% CI = 0.42–0.77; SP = 0.83, 95% CI = 0.76–0.88), GDS‐15 (best cut‐off) (SN = 0.65, 95% CI = 0.40–0.83; SP = 0.72, 95% CI = 0.55–0.85), and Montgomery Asberg Depression Rating Scale (MADRS) (best cut‐off) (SN = 0.77, 95% CI = 0.67–0.85; SP = 0.68, 95% CI = 0.60‐0.75). Conclusions The CSDD continues to have the most evidence for depression case finding in persons living with dementia. The CSDD and Hamilton Depression Rating Scale have the highest sensitivities and may be recommended for use over other common tools like the GDS‐15 and MADRS. Newly identified tools like the NPI‐D and DIA‐S require further study before they can be recommended for use in practice.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.731
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0020.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.071
GPT teacher head0.466
Teacher spread0.395 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations6
Published2024
Admission routes2
Has abstractyes

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