An ecologically sensitive measure of executive cognition (the Breakfast Task) improves prediction of functional outcome in schizophrenia
Bibliographic record
Abstract
Previous work has found that the Breakfast Task (BT), a computerized, ecologically informed executive ability measure, is sensitive to group differences in aging, acquired brain injury, and Parkinson’s disease. We investigated whether this measure improves the prediction of functional status over and above standard measures of general intellectual ability, relationship perception, life skills, and symptom severity in individuals with schizophrenia. Regression analyses were conducted to evaluate the joint and incremental validity of the BT in predicting functional disability scores on the World Health Organization Disability Assessment Scale (WHODAS 2.0). Analyses with combined patient (n = 25) and control (n = 32) participants showed that participant status (patients versus control) was the only significant predictor of functional outcome. However, in the patient data, the proportion of variance accounted for improved significantly (model R2 of 4% vs. 25%) when BT scores were added to a model containing clinical (Brief Psychiatric Rating Scale), social (Relationships Across Domains), intellectual (Wechsler Abbreviated Scale of Intelligence), and life skills (Canadian Objective Assessment of Life Skills) measures. Results suggest that the BT, a tool that captures complex executive functioning, improves the prediction of disability in patients with schizophrenia and has potential assessment applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".