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Record W4307035291 · doi:10.1111/dar.13568

Impulsivity and alexithymia predict early versus subsequent relapse in patients with alcohol use disorder: A 1‐year longitudinal study

2022· article· en· W4307035291 on OpenAlexaboutno aff
Maria Pepe, Marco Di Nicola, Isabella Panaccione, Raffaella Franza, Domenico De Berardis, Mauro Cibin, Luigi Janiri, Gabriele Sani

Bibliographic record

VenueDrug and Alcohol Review · 2022
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsBarratt Impulsiveness ScaleAlexithymiaImpulsivityAbstinenceOdds ratioToronto Alexithymia ScalePsychopathologyMedicinePsychologyPsychiatryRelapse preventionAlcohol dependenceInternal medicineClinical psychologyAlcohol

Abstract

fetched live from OpenAlex

INTRODUCTION: Longitudinal psychopathological predictors of relapse in alcohol use disorder are unclear. METHODS: Relapses, sociodemographic and psychopathological risk factors were assessed in 171 alcohol use disorder outpatients within a 1-year follow up. Impulsivity and alexithymia were evaluated using the Barratt Impulsiveness Scale and the Toronto Alexithymia Scale, respectively. RESULTS: At endpoint, 39% of patients maintained abstinence, 30.9% relapsed at ≤1 month from detoxification (early), 30.1% at >1 month (subsequent). Baseline Barratt Impulsiveness Scale score was predictive of early versus subsequent relapse (odds ratio 1.12, p = 0.005) and versus abstinence (odds ratio 1.17, p < 0.001). Toronto Alexithymia Scale score was a risk factor for subsequent versus early relapse (odds ratio 1.13, p = 0.003) and versus abstinence (odds ratio 1.21, p < 0.001). DISCUSSION AND CONCLUSIONS: Impulsivity predicted relapse within the first 4-weeks; alexithymia showed delayed effects. Time-varying effects of specific relapse factors emphasise the need for preliminary careful assessment and personalised interventions to promote long-term abstinence.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.030
GPT teacher head0.291
Teacher spread0.261 · 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

Citations20
Published2022
Admission routes1
Has abstractyes

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