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Record W72511590

[Olanzapine effects on emotional recognition in treatment refractory schizophrenics].

2004· article· en· W72511590 on OpenAlexaboutno aff
G Y Ibarrarán-Pernas, Miguel Ángel Guevara, L F Cerdán, Julieta Ramos‐Loyo

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsOlanzapineHappinessPsychologyEmpathyRefractory (planetary science)Facial expressionEmotion recognitionClinical psychologySchizophrenia (object-oriented programming)AudiologyPsychotherapistMedicinePsychiatryNeuroscienceCommunication
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: The main purpose of this study was to determine if olanzapine (OLZ) can improve the ability to recognize emotional expressions, in the facial, prosodic and contextual modalities in treatment refractory schizophrenics (TRS) and, if this could be related to its effects on depressive symptoms. METHOD: 14 TRS participated in the study. The Calgary Depression Scale and tasks for recognition of facial, prosodic and contextual emotions were applied prior to and 8 weeks after consuming OLZ. The results were compared to a control group (CO). RESULTS: TRS obtained lower scores than the CO on the recognition of facial and prosodic emotions. They also showed less empathy to the happiness film and they expressed incongruous answers on the contextual emotions. The TRS increased the number of correct responses for the prosodic recognition of happiness and they showed a reduction in their depressive symptomatology after OLZ treatment. CONCLUSIONS: OLZ caused a decrease of the depressive symptoms and improved the interpretation of positive prosodic affective stimuli, an aspect that may facilitate the social adaptation of TRS.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.271
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2004
Admission routes1
Has abstractyes

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