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Record W3026947879 · doi:10.1093/schbul/sbaa030.366

M54. ASSOCIATIONS BETWEEN MUSICAL ABILITY SUBSCALE PERFORMANCE, PSYCHIATRIC SYMPTOMS, AND COGNITIVE FUNCTIONING IN SCHIZOPHRENIA

2020· article· en· W3026947879 on OpenAlexaffabout
Yoshiki Akagawa, William G. Honer, Ken Sawada

Bibliographic record

VenueSchizophrenia Bulletin · 2020
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCognitionSchizophrenia (object-oriented programming)PsychologyVerbal fluency testAudiologyPositive and Negative Syndrome ScalePsychiatryVerbal memoryClinical psychologyPsychosisNeuropsychologyMedicine

Abstract

fetched live from OpenAlex

Abstract Background Several studies showed that patients with schizophrenia have a lower musical ability that correlates with poorer cognitive functions and severer negative symptoms. Despite the strong relevance of musical ability to cognitive functions and psychiatric symptoms, little is known about the correlation of each subscale of musical ability to cognitive functions and psychiatric symptoms. Therefore, we sought to analyze the correlations of the subtests of musical ability to cognitive functions and psychiatric symptoms. Methods Sixty-four patients with schizophrenia (36 males, mean age = 48.6 ± 10.9 years old) and 80 healthy control subjects (44 males, mean age = 45.3 ± 12.3 years old) consented to participate. We measured musical ability, cognitive functions, and symptom severity using the Montreal Battery for Evaluation of Amusia (MBEA), Brief Assessment of Cognition in Schizophrenia (BACS), and Positive and Negative Syndrome Scale (PANSS), respectively. MBEA subscales include melody discrimination, rhythm discrimination, and musical memory. BACS subscales are comprised of verbal memory, working memory, motor speed, word fluency, attention/processing speed, and executive function. We used the Bonferroni correction for multiple comparisons. For the BACS six subscales, and the three musical subscales, we considered p < 0.00278 to be significant (18 tests), and for PANSS three symptom subscale scores and three musical subscales, we considered p < 0.0056 to be significant (9 tests). Results All musical subscale scores of patients were significantly lower than controls. Lower musical ability subscales were correlated with lower cognitive functions in both healthy controls and patients. In schizophrenia, as previously reported, there were associations between lower musical ability subscales, lower cognitive functions, and more severe psychiatric symptoms. In patients with schizophrenia, while melody discrimination was not correlated with cognitive functions, rhythm discrimination was correlated with verbal memory (beta = 0.378, SE= 0.010, t = 3.42, p = 0.0012) and attention/processing speed (beta = 0.433, SE= 0.013, t = 3.20, p = 0.0022) adjusted for age, gender, and years of musical education. PANSS negative symptoms were correlated with melody discrimination (beta = 0.346, SE= 0.051, t = -2.82, p = 0.0066) and rhythm discrimination (beta = 0.3259, SE= 0.045, t = -2.88, p = 0.0056), but not musical memory. Discussion This study revealed an association between performance on rhythm discrimination and both verbal memory and word fluency. Furthermore, more severe negative symptoms were associated with lower abilities in melody and rhythm discrimination. Rhythm discrimination could be associated with language disturbances, possibly providing a new insight into the language and musical deficits contributing to the pathophysiology of schizophrenia.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.284
Teacher spread0.258 · 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".

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Citations1
Published2020
Admission routes2
Has abstractyes

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