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Record W2794716739 · doi:10.1093/schbul/sby017.553

F22. DYSLIPIDEMIA AND INFLAMMATORY MARKERS IN RELATION TO CLINICAL SYMPTOMATOLOGY IN PSYCHOTIC DISORDERS

2018· article· en· W2794716739 on OpenAlexaboutno aff
Sherif M. Gohar, Ingrid Dieset, Nils Eiel Steen, Ragni H. Mørch, Trude Iversen, Vidar M. Steen, Ole A. Andreassen, Ingrid Melle

Bibliographic record

VenueSchizophrenia Bulletin · 2018
Typearticle
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsnot available
Fundersnot available
KeywordsInternal medicineSchizophrenia (object-oriented programming)Positive and Negative Syndrome ScaleSchizoaffective disorderPsychosisDyslipidemiaSchizophreniform disorderMedicineDepression (economics)Major depressive disorderBipolar disorderPsychiatryPsychologyDisease

Abstract

fetched live from OpenAlex

Although the role of lipid disturbances and inflammation in psychotic disorders have been demonstrated in a substantial body of research, the association between clinical symptomatology and these two important pathways has not been studied in detail. The main aim of this study was to investigate the associations between serum lipid levels [total cholesterol (TC), low density lipoprotein (LDL), triglyceride (TG)]; general or specific inflammatory markers [C-reactive protein (CRP), soluble tumor necrosis factor receptor 1(sTNF-R1), osteoprotegerin (OPG), interleukin 1 receptor antagonist (IL-1Ra)]; and clinical symptoms (positive, negative and depressive) in patients with psychotic disorders. The sample is consisted of 652 participants divided in two groups: Schizophrenia, schizophreniform and schizoaffective, (schizophrenia group, N = 344); psychosis NOS, psychotic bipolar I, II and NOS, (non-schizophrenia group, N = 308) recruited consecutively between 2003 and 2015 from five major hospitals in Oslo, Norway, as part of Thematically Organized Psychosis (TOP) Study. The Regional Committee for Medical Research Ethics approved the study. Demographic, clinical and medications data were obtained by clinical interviews and from medical records. SCID-I was used for diagnosis in addition to Positive and Negative Syndrome Scale (PANSS) and Calgary Depression Scale for Schizophrenia (CDSS) to assess symptoms severity. Bivariate and multivariate analyses were performed to evaluate associations between symptom profiles, lipid levels and inflammatory markers. Schizophrenia group showed higher levels of TC and LDL compared to non-schizophrenia group after adjusting for age, gender, BMI, smoking, and medications. TC and LDL were positively correlated with depression, whereas TG and LDL were positively correlated with negative symptoms. CRP and OPG were significantly associated with higher levels of TC and LDL. While, sTNF-R1 showed significant positive correlation only with TG. In multivariate regression, higher LDL was significantly associated with higher age, BMI, depressive severity in addition to two inflammatory markers CRP and OPG. The findings of this study highlight the importance of understanding the interaction between inflammatory markers and lipid, and their relation to clinical profile especially depression in psychotic disorders

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.016
GPT teacher head0.289
Teacher spread0.273 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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