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Record W2914767227 · doi:10.1080/14737175.2019.1580145

Objective and biological markers in bipolar spectrum presentations

2019· review· en· W2914767227 on OpenAlexaff
Trisha Chakrabarty, Lakshmi N. Yatham

Bibliographic record

VenueExpert Review of Neurotherapeutics · 2019
Typereview
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBipolar disorderSpectrum (functional analysis)Computational biologyBiologyNeurosciencePhysicsCognition

Abstract

fetched live from OpenAlex

INTRODUCTION: Subthreshold presentations of bipolarity (BSPs) pose a diagnostic conundrum, in terms of whether they should be conceptualized and treated similarly as traditionally defined bipolar disorders (BD). While it has been argued that BSPs are on a pathophysiologic continuum with traditionally defined BDs, there has been limited examination of biological and objective markers in these presentations to validate this assertion. Areas covered: The authors review studies examining genetic, neurobiological, cognitive and peripheral markers in BSPs, encompassing clinical and non-clinical populations with subthreshold hypo/manic symptoms. Results are placed in the context of previously identified markers in traditionally defined BDs. Expert commentary: There have been few studies of objective and biological markers in subthreshold presentations of BD, and results are mixed. While abnormalities in brain structure/functioning, peripheral inflammatory, and cognitive markers have been reported, it is unclear whether these findings are specific to BD or indicative of broad affective pathology. However, some studies suggest that increased sensitivity to reward and positive stimuli are shared between subthreshold and traditionally defined BDs, and may represent a point of departure from unipolar major depression. Further examination of such markers may improve understanding of subthreshold bipolar presentations, and provide guidance in terms of therapeutic strategies.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.090
GPT teacher head0.407
Teacher spread0.317 · 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.

Study designNot applicable
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

Citations2
Published2019
Admission routes1
Has abstractyes

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