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Record W4392112126 · doi:10.1002/gps.6073

Sociodemographic and clinical characteristics of people with oldest older age bipolar disorder in a global sample: Results from the global aging and geriatric experiments in bipolar disorder project

2024· article· en· W4392112126 on OpenAlexaff
Peijun Chen, Martha Sajatovic, Farren Briggs, Benoit H. Mulsant, Annemiek A. Dols, Ariel Gildengers, Joy Yala, Alexandra J.M. Beunders, Hilary P. Blumberg, Soham Rej, Orestes Vicente Forlenza, Esther Jiménez, Sigfried Schouws, Melis Orhan, Ashley Sutherland, Eduard Vieta, Shang‐Ying Tsai, Kaylee Sarna, Lisa T. Eyler

Bibliographic record

VenueInternational Journal of Geriatric Psychiatry · 2024
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsMcGill UniversityJewish General HospitalUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Center for Advancing Translational SciencesClinical and Translational Science Collaborative of Cleveland, School of Medicine, Case Western Reserve UniversityInternational Society for Bipolar DisordersNIH Clinical CenterCase Western Reserve UniversityNational Institutes of HealthSchool of Medicine, Case Western Reserve University
KeywordsBipolar disorderMedicineLogistic regressionDemographyCross-sectional studyGerontologyPsychologyPsychiatryInternal medicineCognition

Abstract

fetched live from OpenAlex

OBJECTS: Studies of older age bipolar disorder (OABD) have mostly focused on "younger old" individuals. Little is known about the oldest OABD (OOABD) individuals aged ≥70 years old. The Global Aging and Geriatric Experiments in Bipolar Disorder (GAGE-BD) project provides an opportunity to evaluate the OOABD group to understand their characteristics compared to younger groups. METHODS: We conducted cross-sectional analyses of the GAGE-BD database, an integrated, harmonized dataset from 19 international studies. We compared the sociodemographic and clinical characteristics of those aged <50 (YABD, n = 184), 50-69 (OABD, n = 881), and ≥70 (OOABD, n = 304). To standardize the comparisons between age categories and all characteristics, we used multinomial logistic regression models with age category as the dependent variable, with each characteristic as the independent variable, and clustering of standard errors to account for the correlation between observations from each of the studies. RESULTS: OOABD and OABD had lower severity of manic symptoms (Mean YMRS = 3.3, 3.8 respectively) than YABD (YMRS = 7.6), and lower depressive symptoms (% of absent = 65.4%, and 59.5% respectively) than YABD (18.3%). OOABD and OABD had higher physical burden than YABD, especially in the cardiovascular domain (prevalence = 65% in OOABD, 41% in OABD and 17% in YABD); OOABD had the highest prevalence (56%) in the musculoskeletal domain (significantly differed from 39% in OABD and 31% in YABD which didn't differ from each other). Overall, OOABD had significant cumulative physical burden in numbers of domains (mean = 4) compared to both OABD (mean = 2) and YABD (mean = 1). OOABD had the lowest rates of suicidal thoughts (10%), which significantly differed from YABD (26%) though didn't differ from OABD (21%). Functional status was higher in both OOABD (GAF = 63) and OABD (GAF = 64), though only OABD had significantly higher function than YABD (GAF = 59). CONCLUSIONS: OOABD have unique features, suggesting that (1) OOABD individuals may be easier to manage psychiatrically, but require more attention to comorbid physical conditions; (2) OOABD is a survivor cohort associated with resilience despite high medical burden, warranting both qualitative and quantitative methods to better understand how to advance clinical care and ways to age successfully with BD.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.012
GPT teacher head0.325
Teacher spread0.313 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2024
Admission routes1
Has abstractyes

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