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Record W2140239886 · doi:10.1017/s0033291708003073

Is alexithymia a personality trait increasing the risk of depression? A prospective study evaluating alexithymia before, during and after a depressive episode

2008· article· en· W2140239886 on OpenAlexaboutno aff
Carlo Marchesi, S. Bertoni, A. Cantoni, Carlo Maggini

Bibliographic record

VenuePsychological Medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaDepression (economics)PsychologyToronto Alexithymia ScalePsychiatryPersonalityAnxietyClinical psychologyHospital Anxiety and Depression ScaleProspective cohort studyMedicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Whether alexithymia is a personality trait that increases the risk of major depression (MD) is still debated. In this prospective study, alexithymic levels were evaluated before, during and after a depressive episode. METHOD: The alexithymic levels, the presence of MD and the severity of anxious-depressive symptoms were evaluated at intervals of about 1 month in pregnant women attending the Centers for Prenatal Care, using the Toronto Alexithymia Scale (TAS), the Primary Care Evaluation of Mental Disorders (PRIME-MD) and the Hospital Anxiety and Depression Scale (HADS). RESULTS: Sixteen women affected by MD, 21 affected by subthreshold depression and 112 non-depressed women were included in the study. Women who developed depression, compared to non-depressed women, showed similar TAS and HADS scores during the pre-morbid phase, a significant increase in the scores during depression and a significant decrease after remission, whereas no change was observed in non-depressed women. CONCLUSIONS: Our data suggest that in pregnant women alexithymia does not represent a personality trait that increases the risk of developing a depressive episode, and they support the hypothesis that alexithymia is a state-dependent phenomenon in depressed pregnant women.

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 score0.016
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

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

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 head0.039
GPT teacher head0.356
Teacher spread0.317 · 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

Citations62
Published2008
Admission routes1
Has abstractyes

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