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Pure and mixed manic subtypes: a review of diagnostic classification and validation

2008· review· en· W1990525063 on OpenAlexaff
Frederick Cassidy, Lakshmi N. Yatham, Michael Berk, Paul Grof

Bibliographic record

VenueBipolar Disorders · 2008
Typereview
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsManiaHypomaniaBipolar disorderPsychologyCategorical variableNosologyZiprasidoneClinical psychologyPsychiatryMoodQuetiapineStatistics

Abstract

fetched live from OpenAlex

OBJECTIVE: To review issues surrounding the diagnosis and validity of bipolar manic states. METHODS: Studies of the manic syndrome and its diagnostic subtypes were reviewed emphasizing historical development, conceptualizations, formal diagnostic proposals, and validation. RESULTS: Definitions delineating mixed and pure manic states derive some validity from external measures. DSM-IV and ICD-10 diagnosis of bipolar mixed states are too rigid and less restrictive definitions can be validated. Anxiety is a symptom often overlooked in diagnosis of manic subtypes and may be relevant to the mixed manic state. The boundary for separation of mixed mania and depression remains unclear. A 'pure' non-psychotic manic state similar to Kraepelin's 'hypomania' has been observed in several independent studies. CONCLUSIONS: Issues surrounding diagnostic subtyping of manic states remain complex and the debates surrounding categorical versus dimensional approaches continue. To the extent that categorical approaches for mixed mania diagnosis are adopted, both DSM-IV and ICD-10 are too rigid. Inclusion of non-specific symptoms in definitions of mixed mania, such as psychomotor agitation, does not facilitate and may hinder the diagnostic separation of pure and mixed mania. The inclusion of a diagnostic seasonal specifier for DSM-IV, which is currently based on seasonal patterns for depression might be expanded to include seasonal patterns for mania. Boundaries between subtypes may be 'fuzzy' rather than crisp, and graded approaches could be considered. With the continued development of new tools, such as imaging and genetics, alternative approaches to diagnosis other than the purely symptom-centric paradigms might be considered.

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 score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.035
GPT teacher head0.307
Teacher spread0.272 · 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

Citations61
Published2008
Admission routes1
Has abstractyes

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