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Record W2902109961 · doi:10.1136/bmjopen-2018-026598

Diagnostic accuracy of the Geriatric Depression Scale-30, Geriatric Depression Scale-15, Geriatric Depression Scale-5 and Geriatric Depression Scale-4 for detecting major depression: protocol for a systematic review and individual participant data meta-analysis

2018· review· en· W2902109961 on OpenAlexafffund
Andrea Benedetti, Yin Wu, Brooke Levis, Machelle Wilchesky, Jill Boruff, John P. A. Ioannidis, Scott B. Patten, Pim Cuijpers, Ian Shrier, Simon Gilbody, Zahinoor Ismail, Dean McMillan, Nicholas Mitchell, Roy C. Ziegelstein, Brett D. Thombs

Bibliographic record

VenueBMJ Open · 2018
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsJewish General HospitalMcGill UniversityUniversity of AlbertaUniversity of CalgaryMcGill University Health Centre
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchAlberta InnovatesJewish General HospitalAlzheimer Society
KeywordsDepression (economics)Geriatric Depression ScaleMedicineScale (ratio)PsychiatryGeriatricsProtocol (science)Geriatric psychiatryGerontologyDepressive symptomsAlternative medicineCognitionPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: The 30-item Geriatric Depression Scale (GDS-30) and the shorter GDS-15, GDS-5 and GDS-4 are recommended as depression screening tools for elderly individuals. Existing meta-analyses on the diagnostic accuracy of the GDS have not been able to conduct subgroup analyses, have included patients already identified as depressed who would not be screened in practice and have not accounted for possible bias due to selective reporting of results from only better-performing cut-offs in primary studies. Individual participant data meta-analysis (IPDMA), which involves a standard systematic review, then a synthesis of individual participant data, rather than summary results, could address these limitations. The objective of our IPDMA is to generate accuracy estimates to detect major depression for all possible cut-offs of each version of the GDS among studies using different reference standards, separately and among participant subgroups based on age, sex, dementia diagnosis and care settings. In addition, we will use a modelling approach to generate individual participant probabilities for major depression based on GDS scores (rather than a dichotomous cut-off) and participant characteristics (eg, sex, age, dementia status, care setting). METHODS AND ANALYSIS: Individual participant data comparing GDS scores to a major depression diagnosis based on a validated structured or semistructured diagnostic interview will be sought via a systematic review. Data sources will include Medline, Medline In-Process & Other Non-Indexed Citations, PsycINFO and Web of Science. Bivariate random-effects models will be used to estimate diagnostic accuracy parameters for each cut-off of the different versions of the GDS. Prespecified subgroup analyses will be conducted. Risk of bias will be assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. ETHICS AND DISSEMINATION: The findings of this study will be of interest to stakeholders involved in research, clinical practice and policy. PROSPERO REGISTRATION NUMBER: CRD42018104329.

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)0.015
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.714
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.014
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0140.004
Bibliometrics0.0020.005
Science and technology studies0.0020.000
Scholarly communication0.0010.002
Open science0.0050.013
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.283
GPT teacher head0.504
Teacher spread0.221 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreProtocol

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

Citations53
Published2018
Admission routes2
Has abstractyes

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