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Record W2600329571 · doi:10.1093/schbul/sbx023.030

SA31. The Validity of a Computer-Simulated Breakfast Cooking Task as a Predictor of Disability in Patients With Schizophrenia

2017· article· en· W2600329571 on OpenAlexaff
Melissa Parlar, R. Walter Heinrichs, Stephanie McDermid Vaz, Emily Cole

Bibliographic record

VenueSchizophrenia Bulletin · 2017
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsYork University
Fundersnot available
KeywordsPsychologyWechsler Adult Intelligence ScaleBrief Psychiatric Rating ScaleCognitionSchizophrenia (object-oriented programming)PopulationClinical psychologyTest (biology)Predictive validityTask (project management)PsychiatryPsychosisMedicine

Abstract

fetched live from OpenAlex

Background: Patients with schizophrenia experience deficits in multiple domains of functioning. Cognition is often considered a primary predictor of functioning in this population. Standard cognitive test batteries, however, do not always capture substantial variance in functional outcome. This may reflect inadequate sampling of functionality relevant cognitive processes. Tools that incorporate more functionally relevant stimuli may be useful in improving current assessment methods. The current study examines the incremental validity of a rehabilitation cognitive science measure, the Breakfast Task, in predicting disability relative to standard measures of cognition and symptom severity. Methods: Patients with schizophrenia (n = 33) and healthy controls (n = 37) completed the Breakfast Task, a computerized working memory and executive test that simulates cooking breakfast. Participants were required to start and stop cooking 5 foods with the goal of having all of the foods ready at the same time while simultaneously completing a table-setting task. The Brief Psychiatric Rating Scale (BPRS) Positive and Negative Symptom subscales and the Wechsler Abbreviated Scale of Intelligence (WASI) Matrix Reasoning and Vocabulary subtests were administered to assess symptoms and intellectual functioning, respectively. Participants completed the WHO Disability Assessment Schedule 2.0 (WHODAS 2.0) to assess current disability. Group differences on Breakfast Task performance were first assessed. To determine the predictive validity of symptoms, intellectual functioning, and Breakfast Task performance, a hierarchical regression was carried out with predictors entered in the following order: (1) BPRS scores, (2) WASI t-scores, and (3) Breakfast Task scores, with WHODAS 2.0 summary scores as the dependent variable. Results: On the Breakfast Task, patients performed significantly worse than controls in their ability to ensure all 5 foods were completed at the same time (i.e., the Range score; U= 243, P = .001), reflecting working memory and planning deficits. Among patients, the Breakfast Task Range score added significant incremental validity to the prediction of WHODAS 2.0 summary scores, relative to symptoms and current intellectual functioning (Fchange(20) = 4.61, P = .04). The complete model accounted for 23.5% of the variance in disability scores. Conclusion: The Breakfast Task is relatively understudied in patients with schizophrenia. Results suggest that patients are impaired relative to controls on this simulated ecologically relevant task. Performance on this task increases the ability to predict disability beyond that provided by standard cognitive and symptom measures. Our results suggest potential utility of rehabilitation cognitive science measures such as the Breakfast Task in assessing and designing rehabilitation programs for patients with 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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.269
Teacher spread0.256 · 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 designSimulation or modeling
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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Citations0
Published2017
Admission routes1
Has abstractyes

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