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Record W2359220645

Analysis of related factors of alexithymia in depression patients with somatic symptoms

2014· article· en· W2359220645 on OpenAlexaboutno aff
Yang Qi

Bibliographic record

VenueMedical Journal of Chinese People's Health · 2014
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaSomatizationToronto Alexithymia ScaleDepression (economics)Hamilton depression scalePsychologyClinical psychologyInternal medicineMedicinePsychiatryAnxietyHamd
DOInot available

Abstract

fetched live from OpenAlex

Objective:To investigate related factors of alexithymia in depression patients with somatic symptoms. Methods:A case-control study was used,and 130 patients were collected,who met the tenth edition of the international classification of diseases(ICD-l0) diagnostic criteria for depression with a score of 17-item Hamilton depression scale 17 of greater than or equal to 17 points. The patients with the SCL-90 somatization factor score of greater than 2 were chosen as depression with somatic symptoms group(study group),while those with the SCL-90 somatization factor score of less than or equal to 2were used as depression without somatic symptoms group(control group). Alexithymia of all the patients was evaluated with Toronto alexithymia scale(TAS-20) and its related factors were analyzed and compared. Results:(1) study group had higher alexithymia scored,and had the statistical significance(P 0. 01).(2) The higher depression score was,the more severe alexithymia was.(3) TAS factor 1 and 3 symptom factors were positively correlated,factor 2 and 5 symptom factors were positively correlated,while factor 3 and 8 symptom factors were negatively correlated,and they all were statistically significant(P 0. 05). Conclusions:The depression patients with somatic symptoms have obvious alexithymia. Gender,years of education,severity of depression are important factors for these 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)0.001
metaresearch head score (Gemma)0.001
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 score0.006
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.004
GPT teacher head0.277
Teacher spread0.272 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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