MétaCan
Menu
Back to cohort
Record W3025280682 · doi:10.46278/j.ncacn.20190405

Investigation of Emotional Expression Processing Following Cognitive Behavioural Therapy for Patients with Schizophrenia: An Event-Related Potentials Study

2019· article· en· W3025280682 on OpenAlexaffvenue
Dhrasti Shah, Verner Knott, Ashley Baddeley, Hayley Bowers, Nicola Wright, Allen Labelle, Dylan Smith, Charles A. Collin

Bibliographic record

VenueNeuropsychologie clinique et appliquée · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of GuelphRoyal Ottawa Mental Health CentreUniversity of Ottawa
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)CognitionPsychologyPsychosisEvent-related potentialFacial expressionEmotional expressionBackward maskingClinical psychologyAudiologyPsychiatryMedicineDevelopmental psychologyNeurosciencePerception

Abstract

fetched live from OpenAlex

Growing evidence supports the use of cognitive behavioural therapy (CBT) for psychosis, including CBT for voices (CBTv), which targets auditory verbal hallucinations (AVH). The present study observed the effects of CBTv on electrophysiological measures of facial expression processing in patients with schizophrenia with AVH. Twenty-five patients with schizophrenia were randomly assigned to a treatment group (TG; n = 14) or a treatment as usual (TAU) group (n = 11). The TG received group CBTv for five-six months in addition to their TAU. The matched waitlist group received TAU for the five-six months. The CBTv treatment showed shorter P100 latency in response to facial expressions following treatment compared with baseline, but not the TAU group. Amount of negative content of voices and “omnipotence” of voices were modified following CBTv treatment, but not following TAU. This study provides evidence that CBTv decreases early visual information processing time as indexed by the P100 latency.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.077
GPT teacher head0.370
Teacher spread0.293 · 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
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueNeuropsychologie clinique et appliquéeSame topicNeuroscience and Music PerceptionFrench-language works237,207