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Record W3112182438 · doi:10.1177/2050312120981178

COVID-19: Implications for bipolar disorder clinical care and research

2020· review· en· W3112182438 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSAGE Open Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsBipolar disorderMedicinePsychiatrySocial isolationContext (archaeology)Isolation (microbiology)PandemicMental healthIntervention (counseling)Health careCoronavirus disease 2019 (COVID-19)DiseaseCognition

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has posed significant challenges to health care globally, and individuals with bipolar disorder are likely disproportionally affected. Based on review of literature and collective clinical experience, we discuss that without special intervention, individuals with bipolar disorder will experience poorer physical and mental health outcomes due to interplay of patient, provider and societal factors. Some risk factors associated with bipolar disorder, including irregular social rhythms, risk-taking behaviours, substantial medical comorbidities, and prevalent substance use, may be compounded by lockdowns, social isolation and decrease in preventive and maintenance care. We further discuss implications for clinical research of bipolar disorders during the pandemic. Finally, we propose mitigation strategies on working with individuals with bipolar disorder in a clinical and research context, focusing on digital medicine strategies to improve quality of and accessibility to service.

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.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.326
GPT teacher head0.583
Teacher spread0.258 · 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