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Clinical correlates of current level of functioning in primary care‐treated bipolar patients

2005· article· en· W2076298345 on OpenAlexafffund
Tomáš Hájek, Claire Slaney, Julie Garnham, Martina Růžičková, Michael J. Passmore, Martin Alda

Bibliographic record

VenueBipolar Disorders · 2005
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsDalhousie University
FundersDalhousie University
KeywordsGlobal Assessment of FunctioningBipolar disorderComorbidityPsychiatryMedicineClinical psychologyPsychologyMoodSchizophrenia (object-oriented programming)

Abstract

fetched live from OpenAlex

OBJECTIVES: In this study we examined general assessment of functioning (GAF), and its relation to clinical and demographic factors in bipolar patients. A number of studies, mostly from specialized programs, show that bipolar disorder often leads to occupational and social impairment. Here we report data from patients in a primary care setting. METHODS: A total of 252 patients from the Maritime Bipolar Registry with DSM-IV diagnoses of bipolar I or bipolar II disorder participated in the study. GAF ratings during maintenance treatment were compared across clinical and demographic variables. RESULTS: The mean GAF score in this sample was 67 +/- 17 (range 10-100). The GAF scores followed bimodal distribution with mean values of 50.5 +/- 10.3 and 79.0 +/- 10.3. Decreased functioning was found in patients with chronic illness course, history of rapid cycling, suicidal behaviour, psychiatric comorbidity, hypothyroidism, and diabetes mellitus, regardless of treatment of these conditions. There were no differences in the level of functioning between men and women, bipolar I and II patients, those with and without psychotic episodes, hypertension, treatment with antidepressants or antipsychotics. CONCLUSIONS: Functioning in primary care-treated bipolar patients in maintenance phase of treatment is decreased not only due to specific disorder-related variables, but also due to frequent comorbidity with other psychiatric and medical conditions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.912

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 head0.042
GPT teacher head0.307
Teacher spread0.265 · 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

Citations66
Published2005
Admission routes2
Has abstractyes

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