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Medical and substance-related comorbidity in bipolar disorder: translational research and treatment opportunities

2008· article· en· W4298309127 on OpenAlexaff
Roger S. Mclntyre, Há Thi Nguyen, Joanna K. Soczynska, Maria Teresa Cruz Lourenço, Hanna O. Woldeyohannes, Jakub Z. Konarski

Bibliographic record

VenueDialogues in Clinical Neuroscience · 2008
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsBipolar disorderComorbidityDiseasePsychiatryDiabetes mellitusPsychologyMedicineNeuroscienceClinical psychologyCognitionInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

It is well established that individuals with bipolar disorder are differentially affected by substance-related as well as medical disorders (ie, cardiometabolic disorders, respiratory disorders, neurological disorders, and infectious diseases). Emerging evidence indicates that some comorbid conditions (eg, diabetes mellitus) in bipolar individuals may be subserved by overlapping neurobiological networks. Disturbances in glucocorticoid/insulin signaling and immunoinflammatory effector systems are points of pathophysiological commonality between bipolar disorder and “stress-sensitive” medical disorders. Subphenotyping bipolar disorder as a function of comorbidity and temporality of onset may provide an opportunity for refining disease pathophysiological models and developing innovative disease-modifying therapies.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.378
GPT teacher head0.437
Teacher spread0.059 · 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 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

Citations33
Published2008
Admission routes1
Has abstractyes

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