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Record W7081523333 · doi:10.1177/104012371202400106

The Canadian Network for Mood and Anxiety Treatments (Canmat) Task Force Recommendations for the Management of Patients with Mood Disorders and Comorbid Personality Disorders

2012· article· en· W7081523333 on OpenAlexaffabout

Bibliographic record

VenueAnnals of Clinical Psychiatry · 2012
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsSunnybrook Health Science CentreUniversity of CalgaryDouglas Mental Health University InstituteToronto East General HospitalUniversity Health NetworkSunnybrook Hospital
Fundersnot available
KeywordsMoodPersonality disordersPersonalityMood disordersAnxietyExtant taxonComorbidityMEDLINE

Abstract

fetched live from OpenAlex

Background The association between mood disorders and personality disorders (PDs) is complicated clinically, conceptually, and neurobiologically. There is a need for recommendations to assist clinicians in treating these frequently encountered patients. Methods The literature was reviewed with the purpose of identifying clinically relevant themes. MedLine searches were supplemented with manual review of the references in relevant papers. From the extant evidence, consensus-based recommendations for clinical practice were developed. Results Key issues were identified with regards to the overlap of PDs and mood disorders, including whether certain personality features predispose to mood disorders, whether PDs can reliably be recognized if there is an Axis I disorder present, whether personality disturbances arise as a consequence or are a forme fruste of mood disorders, and whether personality traits or disorders modify treatment responsiveness and outcome of mood disorders. Conclusion This paper describes consensus-based clinical recommendations that arise from a consideration of how signals from the literature can impact clinical practice in the treatment of patients with comorbid mood and personality pathology. Additional treatment studies of patients with the comorbid conditions are required to further inform clinical practice.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.579
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.048
GPT teacher head0.342
Teacher spread0.294 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2012
Admission routes2
Has abstractyes

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