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Record W3006305683 · doi:10.3389/fpsyt.2019.00991

Alexithymia and Treatment Outcome in Anorexia Nervosa: A Scoping Review of the Literature

2020· review· en· W3006305683 on OpenAlexaboutno aff
Carla Gramaglia, Eleonora Gambaro, Patrizia Zeppegno

Bibliographic record

VenueFrontiers in Psychiatry · 2020
Typereview
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaAnorexia nervosaPsychological interventionPsychotherapistPsychologyClinical psychologyIntervention (counseling)Toronto Alexithymia ScaleEating disordersPsychiatryMedicine

Abstract

fetched live from OpenAlex

Alexithymia is of great interest as an outcome predictor of recovery from anorexia nervosa, since it may interfere with both treatment compliance and patients' ability to benefit from the adopted interventions. For this reason, in the last years new treatment approaches targeting emotion identification, expression, and regulation have been applied and tested. Using the PRISMA methodology, we performed a scoping review of the literature about treatment outcome in anorexia nervosa, in terms of changes in alexithymia as assessed by its most commonly used self-report measure, the Toronto Alexithymia Scale (TAS). The Medline and Scopus databases were searched, and articles were included if matching the following criteria: dealing with patients affected by anorexia nervosa, without limits of age; involving the application of any kind of targeted therapy or treatment; assessing alexithymia and the effect of a treatment intervention on alexithymia, using the TAS. Ten studies were eventually included; overall, according to the selected studies, alexithymia levels often remain high even after specific treatment. Further research aimed at a deeper understanding of the actual impact of alexithymia on the outcome of anorexia, as well as exploring alternative treatment strategies for alexithymia in eating disorders (EDs), are warranted.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.694
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.367
Teacher spread0.335 · 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 designSystematic review
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

Citations31
Published2020
Admission routes1
Has abstractyes

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