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Record W2795363662 · doi:10.1093/schbul/sby018.905

S118. CAN THE POSITIVE AND NEGATIVE SYNDROME SCALE (PANSS) DIFFERENTIATE REFRACTORY FROM NON-REFRACTORY SCHIZOPHRENIA? A FACTOR ANALYTIC INVESTIGATION BASED ON DATA FROM THE PATTERN COHORT STUDY

2018· article· en· W2795363662 on OpenAlexaff
Rosana de Freitas, Bernardo dos Santos, Carlo Altamura, Corrado Bernasconi, Ricardo Corral, Jonathan Evans, Ashok Malla, Marie‐Odile Krebs, Anna-Lena Nordstroem, Mathias Zink, Josep María Haro, Hélio Elkis

Bibliographic record

VenueSchizophrenia Bulletin · 2018
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsPositive and Negative Syndrome ScalePsychologySchizophrenia (object-oriented programming)Clinical psychologyConfirmatory factor analysisPsychiatryPsychopathologyVarimax rotationStructural equation modelingInternal medicinePsychosisMedicinePsychometricsStatisticsCronbach's alpha

Abstract

fetched live from OpenAlex

Treatment Resistant Schizophrenia (TRS) and Non-Treatment Resistant Schizophrenia (NTRS) may represent different biological subtypes of schizophrenia but there are few studies which investigated the distinction between these groups in terms of psychopathology. In the present study, we used both Exploratory (EFA) and Confirmatory (CFA) Factor Analyses to investigate symptom dimensions in TRS in comparison with NTRS using the Positive and Negative Syndrome Scale (PANSS). Data from 1429 patients who participated in the PATTERN study a Non- Intervention Prospective Study of Patients with Persistent Symptoms of Schizophrenia) was used. TRS was defined by proxy, based on the use of clozapine (TRS) whereas NTRS used non-clozapine antipsychotics (NTRS). EFA methods included the extraction of principal components and the Varimax rotation. The number of factors was chosen based on the Kaiser criterion. Factors items were considered valid when loadings were greater or equal to 0.5. The fit to the data was evaluated by CFA in comparison with well established PANSS models using fit indexes such as: NNFI (Non-Normed Fit Index), NFI (Normed fit Index), CFI (Comparative Fit Index), RMEA (Root-Mean-Square Error of Approximation). SPSS 23.0 and R version 3.2.2 were used for statistical analyses. Demographic data showed that, when compared with NTRS, patients with TRS showed an earlier age of onset, a longer duration of illness, higher PANSS positive scores, a higher duration of persistent positive and negative symptoms. There were no differences between groups in terms of the duration of untreated psychosis. The EFA yielded almost the same five-factor structure in both groups namely Negative, Positive, Affective, Disorganized/Cognitive and Excitation factors. CFA showed that both models do not fit completely to the data when compared with well known PANSS factor analytical models. Data from a large cross-national sample of 1429 patients of the Pattern study showed that TRS and NTRS patients have an almost identical factor structure when evaluated by the PANSS. These results are similar to a previous study with a smaller sample which has evaluated the dimensions of the PANSS in patients with refractory schizophrenia.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.027
GPT teacher head0.277
Teacher spread0.249 · 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; both teacher heads agree on what is shown here.

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
Published2018
Admission routes1
Has abstractyes

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