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Record W3028902347 · doi:10.17116/jnevro202012004136

A study of alexithymia in schizophrenia and some somatic diseases

2020· article· en· W3028902347 on OpenAlexaboutno aff
T.A. Kafarov, N. A. Aliev, Nadir A. Aliyev

Bibliographic record

VenueS S Korsakov Journal of Neurology and Psychiatry · 2020
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaSchizophrenia (object-oriented programming)MedicineNeuroticismPsychiatryToronto Alexithymia ScaleDiseaseClinical psychologyInternal medicinePsychologyPersonality

Abstract

fetched live from OpenAlex

OBJECTIVE: To study alexithymia in schizophrenia and some somatic diseases. MATERIAL AND METHODS: The study included 60 patients with paranoid schizophrenia (ICD-10 F20.0), 55 hypertensive patients with primary heart disease (I11), 53 patients with chronic ischemic heart disease (I25) and 51 patients with insulin-dependent diabetes mellitus (E10). To assess the manifestations of alexithymia, the Toronto alexithymia scale (TAS-26) was used. The Positive and negative syndrome scale (PANSS) was administered to patients with schizophrenia. RESULTS: The average scores on TAS were higher in patients with schizophrenia compared to patients with somatic diseases (109,73 and 81,66, respectively). During the analysis of responses to TAS statements, features of a general and particular nature were identified. CONCLUSIONS: Alexithymia is not a property characteristic of psychosomatic diseases and neurotic disorders, and is detected in schizophrenic patients and patients with somatic diseases. The relationship of alexithymia with negative symptoms of schizophrenia suggests that it might impact on interpersonal relations, social adaptation and development of autistic symptoms in patients.

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 score0.019
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.015
GPT teacher head0.264
Teacher spread0.250 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueS S Korsakov Journal of Neurology and PsychiatrySame topicPsychosomatic Disorders and Their TreatmentsFrench-language works237,207