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Record W2161905994 · doi:10.1002/hup.612

Comorbidity in bipolar disorder: a framework for rational treatment selection

2004· review· en· W2161905994 on OpenAlexaff
Roger S. McIntyre, Jakub Z. Konarski, Lakshmi N. Yatham

Bibliographic record

VenueHuman Psychopharmacology Clinical and Experimental · 2004
Typereview
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity of British ColumbiaUniversity Health Network
Fundersnot available
KeywordsBipolar disorderSchema (genetic algorithms)Bipolar illnessPsychologyComorbidityInterimPsychotherapistPsychiatryMoodTreatment of bipolar disorderMood disordersClinical psychologyComputer scienceManiaPolitical scienceInformation retrieval

Abstract

fetched live from OpenAlex

Bipolar disorders are heterogeneous disorders often requiring multimodality treatment. The expanding pharmacopeia for bipolar disorders invites the need for a treatment framework that both recognizes and anticipates the multidimensionality and comorbidity of the illness. No available neurotherapeutic agent is singularly efficacious for the complete mélange of bipolar symptomatology. An apparent paradox has emerged in the management of bipolar disorder; whilst results from rigorous controlled monotherapy trials suggest that a disparate assortment of neurotherapeutic agents are efficacious in distinct phases of bipolar disorder, the majority of tertiary-treated bipolar patients receive polypharmacotherapeutic regimens. The evidentiary base for polypharmacotherapy is sparse and has recently become an area of active research focus. In the interim, clinicians are encouraged to invoke an organizational schema for the treatment of bipolar disorder that considers the spectrum of effectiveness of putative and established mood stabilizers. This schema should be further informed by the treatment data for comorbid and accessory conditions. The authors propose a schema to provide the impetus for further work in the area.

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 score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.125
GPT teacher head0.515
Teacher spread0.391 · 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

Citations72
Published2004
Admission routes1
Has abstractyes

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