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Record W4291021037 · doi:10.1055/a-1882-6544

Alexithymie bei Multipler Sklerose – eine narrative Übersicht

2022· review· de· W4291021037 on OpenAlexaboutno aff
Christina Grigorescu, Moussa A. Chalah, Samar S. Ayache, Ulrich Palm

Bibliographic record

VenueFortschritte der Neurologie · Psychiatrie · 2022
Typereview
Languagede
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaPsychologyClinical psychologyCognitionAnxietyToronto Alexithymia ScaleEtiologyMedicinePsychiatryGynecology

Abstract

fetched live from OpenAlex

Alexithymia is a multidimensional construct of personality implicating difficulties in identifying and describing another's feelings, and externally oriented thinking. It is broadly reported in psychiatric patients but has gained little attention regarding its occurrence and pathophysiology in multiple sclerosis (MS). This narrative review aims to address prevalence, etiology, neurobiological, and clinical findings of alexithymia. The prevalence of alexithymia in MS ranges from 10 to 53%. There seems to be an association with anxiety, depression, fatigue, and some aspects of social cognition, while the relationship with clinical and classical cognitive variables was rarely evaluated. Only a few studies referred to its pathophysiology assuming an aberrant interhemispheric transfer or regional cerebral abnormalities. The prevalence of alexithymia in MS and the potential negative impact on quality of life and interpersonal communication could severely impact clinical MS management and a screnning for these factors should be mandatory. Thus, further evaluation is needed concerning its relationship with clinical, emotional, and cognitive confounders. Large-scale studies employing neuroimaging techniques are needed for a better understanding of the neural underpinnings of this MS feature.

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), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.408
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0120.003

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 head0.056
GPT teacher head0.327
Teacher spread0.271 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2022
Admission routes1
Has abstractyes

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