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Record W4405130938

The issue of neuropsychiatric disorders in patients with hypothyroidism

2016· article· en· W4405130938 on OpenAlexaboutno aff
Kutashov V.A., Budnevsky A.V., Ulyanova O.V., Priputnevich D.N.

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldMedicine
TopicHuman Health and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychiatryPsychologyPediatrics
DOInot available

Abstract

fetched live from OpenAlex

The aim of the study was assessment of thyroid function in patients with psychiatric and psychological assistance; to study the structure of AR in patients with hypothyroidism, to reveal alexithymical identifying of personality characteristics of patients with hypothyroidism and AP Materials and Methods. 406 patients with AR at the age of 51.3±4.7 years. To verify the useofTSH hypothyroidism rate and to assess depressive disorder — Hamilton Scale and the Montgomery— Asberg; with clinical and psychopathological study: 1) the scale of self-BH alarm Spielberger (Y. L. Hanin), 2) questionnaire G. Shmisheka 3) Toronto alexithymia scale. Results. Hypothyroidism is set at 116 (28.6%) of 406 patients suffering from RA. Among the 374 patients with depressive disorders hypothyroidism was observed in 116: subclinical — in 76 patients (65.5%), symptomatic — in 40 (34.5%). The first group consisted of 258 patients with the presence of depressive disorders and without hypothyroidism; second — 116 patients with the presence of depression and hypothyroidism. TSH in patients suffering AR, without thyroid dysfunction was 5.2 times lower than in patients with the presence of hypothyroidism. Among AP comorbid with hypothyroidism, are predominant depressive with a predominance of mild and moderate forms. Dominating are anxious-depressive, dysphoric, adynamic, sad, depressive disorders. Conclusion. In patients with AR and hypothyroidism predominant types of personality accentuation are dysthymic, anxious, demonstrative and meticulous.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.089
GPT teacher head0.496
Teacher spread0.407 · 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.

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
Published2016
Admission routes1
Has abstractyes

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