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Record W3138428632 · doi:10.1136/bmj.n532

Comparative efficacy of interventions for reducing symptoms of depression in people with dementia: systematic review and network meta-analysis

2021· review· en· W3138428632 on OpenAlexaff
Jennifer Watt, Zahra Goodarzi, Areti Angeliki Veroniki, Vera Nincic, Paul A. Khan, Marco Ghassemi, Yonda Lai, Victoria Treister, Yuan Thompson, Raphaël Schneider, Andrea C. Tricco, Sharon E. Straus

Bibliographic record

VenueBMJ · 2021
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHotchkiss Brain InstituteFoothills Medical CentreUniversity of TorontoUniversity of CalgarySt. Michael's Hospital
Fundersnot available
KeywordsDementiaCochrane LibraryMedicineMeta-analysisDepression (economics)Psychological interventionPsychiatryMEDLINECINAHLInternal medicineDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the comparative efficacy of drug and non-drug interventions for reducing symptoms of depression in people with dementia who experience depression as a neuropsychiatric symptom of dementia or have a diagnosis of a major depressive disorder. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Medline, Embase, the Cochrane Library, CINAHL, PsycINFO, and grey literature between inception and 15 October 2020. ELIGIBILITY CRITERIA FOR STUDY SELECTION: Randomised trials comparing drug or non-drug interventions with usual care or any other intervention targeting symptoms of depression in people with dementia. MAIN OUTCOME MEASURES: Pairs of reviewers screened studies, abstracted aggregate level data, and appraised risk of bias with the Cochrane risk of bias tool, which facilitated the derivation of standardised mean differences and back transformed mean differences (on the Cornell scale for depression in dementia) from bayesian random effects network meta-analyses and pairwise meta-analyses. RESULTS: Of 22 138 citations screened, 256 studies (28 483 people with dementia) were included. Missing data posed the greatest risk to review findings. In the network meta-analysis of studies including people with dementia without a diagnosis of a major depressive disorder who were experiencing symptoms of depression (213 studies; 25 177 people with dementia; between study variance 0.23), seven interventions were associated with a greater reduction in symptoms of depression compared with usual care: cognitive stimulation (mean difference -2.93, 95% credible interval -4.35 to -1.52), cognitive stimulation combined with a cholinesterase inhibitor (-11.39, -18.38 to -3.93), massage and touch therapy (-9.03, -12.28 to -5.88), multidisciplinary care (-1.98, -3.80 to -0.16), occupational therapy (-2.59, -4.70 to -0.40), exercise combined with social interaction and cognitive stimulation (-12.37, -19.01 to -5.36), and reminiscence therapy (-2.30, -3.68 to -0.93). Except for massage and touch therapy, cognitive stimulation combined with a cholinesterase inhibitor, and cognitive stimulation combined with exercise and social interaction, which were more efficacious than some drug interventions, no statistically significant difference was found in the comparative efficacy of drug and non-drug interventions for reducing symptoms of depression in people with dementia without a diagnosis of a major depressive disorder. Clinical and methodological heterogeneity precluded network meta-analysis of studies comparing the efficacy of interventions specifically for reducing symptoms of depression in people with dementia and a major depressive disorder (22 studies; 1829 patients). CONCLUSIONS: In this systematic review, non-drug interventions were found to be more efficacious than drug interventions for reducing symptoms of depression in people with dementia without a major depressive disorder. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017050130.

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)0.031
metaresearch head score (Gemma)0.079
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: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.031
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.079
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0260.037
Bibliometrics0.0110.008
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.168
GPT teacher head0.477
Teacher spread0.308 · 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

Citations141
Published2021
Admission routes1
Has abstractyes

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